Pith. sign in

REVIEW 5 major objections 5 minor 127 references

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read HFF-Net claims that splitting brain MRI into low- and high-frequency branches lifts enhancing-tumor Dice by 5–8 points over recent state-of-the-art baselines.

desk verdict Interesting frequency-domain architecture; the reported Dice gains look inflated until the branch-selection rule and baseline protocol are clarified. read the letter →

arxiv 2506.10142 v1 pith:USGOO2BQ submitted 2025-06-11 eess.IV cs.CV

Minye Shao , Zeyu Wang , Haoran Duan , Yawen Huang , Bing Zhai , Shizheng Wang , Yang Long , Yefeng Zheng This is my paper
classification eess.IVcs.CV
keywords braintumorsegmentationfrequencydomaincontrast-enhancingdual-treecomplexwavelettransformnonsubsampledcontourletcross-attentionBraTS
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper proposes HFF-Net, a brain tumor segmentation network that explicitly decomposes multi-modal MRI into low- and high-frequency components before learning to segment. It claims that treating these components separately—smooth contours in the low-frequency branch, directional textures and edges in the high-frequency branch—remedies the persistent degradation of contrast-enhancing tumor (ET) segmentation. On four public datasets, the paper reports consistent Dice gains, averaging 4.48% relative on mean tumor-region Dice and 7.33% relative on enhancing-tumor Dice. The contribution is not just a new architecture but a claim about where the difficulty lies: spatial-domain-only features miss the textural and directional cues that distinguish enhancing tumor boundaries.

What carries the argument

The argument stands on three modules. FDD (Frequency Domain Decomposition) uses DTCWT for the low-frequency branch, which provides approximate shift invariance, and NSCT for the high-frequency branch, which gives aliasing-free directional sub-bands; the key identity is the Hilbert-pair energy condition |φ̂r(ω)|² + |φ̂i(ω)|² ≈ 1 that makes the low-frequency representation stable under shifts. ALC (Adaptive Laplacian Convolution) initializes convolution kernels with a discrete Laplacian operator and uses Fisher-information Z-score thresholding to freeze important weights, preserving a high-pass filter while adapting the rest. FDCA (Frequency Domain Cross-Attention) transforms features via FFT, applies semantic, positional, and slice attention in the frequency domain, and returns via IFFT. All three modules are jointly trained under a loss that sums supervised Dice on each branch with an unsupervised 3D Dynamic Focal Loss aligning the two branches.

What would settle it

Re-run S2CA-Net and nnUNet on BraTS2020 under HFF-Net's exact data splits, patch size (128×128×128), training length (350 epochs), and inference protocol; if their ET Dice rises to within the reported margin, the frequency-domain attribution is not supported.

Watch

Extended reading notes

Core claim

On the paper's own terms, HFF-Net achieves large, consistent Dice improvements over strong baselines by harmonizing low- and high-frequency information from two classical transforms—DTCWT for low-frequency structure and NSCT for multi-directional high-frequency texture—through three coupled modules: Frequency Domain Decomposition, Adaptive Laplacian Convolution, and Frequency Domain Cross-Attention, trained with a 3D Dynamic Focal Loss that aligns the two branches. The strongest reported result is 96.16 enhancing-tumor Dice on BraTS2023-MEN versus 88.87 for S2CA-Net, and 87.36 versus 80.41 on BraTS2020.

Load-bearing premise

The reported superiority over baselines rests on comparing HFF-Net's numbers against baseline numbers taken from other papers, without a shared, re-run experimental protocol; the reader must assume that protocol differences in patches, epochs, and inference settings do not account for the gain.

Editorial extensions

If this is right

  • If the reported gains hold under a shared protocol, frequency decomposition becomes a cheap, architecture-agnostic preprocessing that any segmentation backbone could adopt.
  • The 2D variant's success on four non-brain datasets suggests the frequency-decoupling principle transfers to other imaging modalities with low-contrast boundaries.
  • Clinical ET segmentation at Dice above 95% on meningioma could reduce manual correction in radiotherapy target delineation.
  • The dual-branch consistency loss offers a route to enforce robustness when one input modality is corrupted, though the paper's own failure cases show this is not yet solved.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A substantial part of the reported margin may come from the dual-branch capacity, patch size (128³), and training protocol (350 epochs, warm-up) rather than from frequency decomposition per se; the paper does not ablate the architecture keeping the total parameter count fixed.
  • The Fisher-information freezing in ALC is a single-task application of a continual-learning mechanism; its effect could be replicated by a fixed Laplacian filter, so an ablation replacing EWC with a plain frozen kernel would isolate its contribution.
  • We would expect the ET gains to shrink if baseline methods were re-run under HFF-Net's exact patch and epoch settings; a shared-protocol benchmark is the natural next test.
  • The reported frequency-domain entropy and shift-invariance metrics are diagnostic, not predictive; they do not yet establish a causal link between decomposition quality and segmentation outcome.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper proposes HFF-Net, a dual-branch network for brain tumor segmentation that decomposes multimodal MRI into low-frequency (DTCWT) and high-frequency (NSCT) components, then processes them with an Adaptive Laplacian Convolution (ALC) layer, Frequency Domain Cross-Attention (FDCA), and a 3D Dynamic Focal Loss consistency term. The authors report large Dice improvements over prior methods, especially for enhancing tumor (ET), on BraTS2019, BraTS2020, BraTS2023-MEN, MSD-BTS, and additional cross-domain datasets, with ablations, visualizations, and failure-case analyses supporting the frequency-domain design.

Significance. If the reported gains survive a shared-protocol comparison, the frequency-decomposition design is a plausible and potentially useful direction for improving ET boundary segmentation. The paper is strong in breadth: it includes four 3D brain datasets, two additional 3D datasets, four 2D datasets, component ablations, operator ablations, decomposition-strategy ablations, Grad-CAM and t-SNE analyses, and a code link. However, the central numerical claim is not currently established because the main comparisons use baselines quoted from prior publications rather than re-run under identical conditions, and the final-branch selection rule at inference is unspecified.

major comments (5)
  1. [§III-A, §III-E] Section III-A states that 'the final segmentation result y is selected from the optimal primary branch prediction' and Section III-E states that it is 'determined by comparing and selecting the superior main output from the two branches.' The manuscript does not specify any inference-time criterion for this selection: there is no confidence score, uncertainty estimate, fusion rule, or ablation of selection strategies. If the selection uses ground-truth labels, then every reported number, including the 96.16% ET Dice on BraTS2023-MEN, is an oracle upper bound rather than a model output, and the claimed 'end-to-end inference without any post-processing' is not supported. Please specify the selection criterion used, or change the inference to a fixed branch or deterministic fusion, and report results under that protocol.
  2. [§IV-C, Tables I–IV] The main tables compare HFF-Net with baseline numbers taken from prior publications rather than results re-run under the same protocol. The implementation details in Section IV-B (350 epochs, 128³ patches, learning-rate schedule, λ_max=15, warm-up, DTCWT/NSCT settings) are specific to HFF-Net, so the reported gains of 4.48% mean Dice and 7.33% ET Dice cannot be attributed to the frequency-domain design without a shared experimental protocol. Please re-run all compared methods under identical data splits, preprocessing, patch extraction, training length, and inference settings, or provide a quantified analysis of protocol sensitivity.
  3. [§IV-A, §IV-C, Table V] Section IV-A describes BraTS2023-MEN as split into 80% training, 15% validation, and 5% testing, while Table V states that 'all experiments were conducted using five-fold cross-validation.' The HFF-Net numbers also differ between Table I and Table V (e.g., ET 96.16 vs 96.1, TC 96.34 vs 96.1). Please clarify which evaluation protocol produced which table and report a single consistent protocol with per-fold results and standard deviations for each dataset.
  4. [§III-D, Table IX] Section III-D says the DTCWT decomposition level is one and later says NSCT uses 'two levels for both pyramid and directional decomposition,' but Table IX identifies the best configuration as [1,4] (one level, four directions) and states that two-level setups perform worse. The exact FDD configuration is therefore ambiguous. Please align the method description with the code and report the precise filter banks and decomposition levels used for the main results.
  5. [§III-E, Eqs. (19)–(22)] The 3D Dynamic Focal Loss is not reproducible from the text as written: D in Eq. (20) is already a sum over u,v,w, and Eq. (22) then multiplies this sum by per-frequency weights and divides by N, which is not a weighted average of amplitude distance and dynamic weights. Please provide the exact tensor operations, with shapes and the role of α, and clarify whether the 'prediction from one branch as pseudo-label' is applied in the frequency or spatial domain.
minor comments (5)
  1. [Tables III and VII] Table III reports 36.01M parameters for HFF-Net while Table VII reports 37.76M for the full model; please reconcile these numbers.
  2. [Figure 10(b)] A throughput of 6.71 images/s corresponds to about 149 ms per image, not the reported 159.3 ms; please correct the inconsistency.
  3. [Table VIII] Table VIII reports single-run results without standard deviations; the 0.58% ET gap between the Kirsch and discrete Laplace operators is small relative to the standard deviations in Table V, so statistical significance should be assessed.
  4. [Table VI] Cross-dataset results on LiTS and LA are reported without error bars or number of runs; since the test sets are small, repeated-run statistics are needed to support the claimed improvements.
  5. [Eq. (17)] The symbol ŷ^ft_i uses the superscript t before it is defined; please define t and the side-output subscript explicitly.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the frequency-domain modules are designed from signal-processing principles and validated by ablations; the only concern is an unspecified branch-selection criterion that could, if implemented via ground-truth Dice, become oracle selection, but the paper does not define it as such.

full rationale

The derivation chain of HFF-Net is self-contained: FDD applies fixed DTCWT/NSCT transforms; ALC uses a Laplacian-initialized kernel regularized by Fisher information; FDCA performs attention in the Fourier domain; and the losses are supervised Dice plus an unsupervised consistency term. None of these modules are fitted to the evaluation metric or derived from the reported Dice gains. Ablations (Tables VII-IX) independently establish the contribution of each component by comparing configurations on fixed datasets. The paper does cite the authors' own prior work (e.g., [32], [38], [39], [41]), but these citations are contextual, not load-bearing: the core frequency-domain claims are supported by standard signal-processing references [31], [80] and by the paper's own experiments. The one point requiring scrutiny is the final output selection: Section III-E states that the 'superior' of the two branch outputs is chosen, but no inference-time criterion (e.g., confidence, uncertainty) is defined. If 'superior' were resolved by comparing branch Dice against ground-truth labels, the reported numbers would be oracle maxima rather than predictions; however, the paper does not state that this is done, and the same section describes an unsupervised consistency loss that couples the branches, making such a selection non-independent. This ambiguity is a reporting gap that should be clarified, but it is not, from the text alone, a demonstrated circularity. Overall, the central claim that frequency decomposition helps segmentation is supported by multiple independent ablations and by the architecture's consistency with known transform properties.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

No new physical or mathematical entities are introduced. The ledger is dominated by unstated hyperparameters and design choices that could affect results but are not swept or reported.

free parameters (4)
  • Fisher threshold scaling k in Z-score mask = not stated
    k controls how many ALC kernel weights are frozen; the paper never states its value or sensitivity.
  • lambda_max schedule multiplier = 15 for BraTS, 5 for LA, 0.5 to 2 for 2D datasets
    Dataset-specific values chosen by hand; no sensitivity analysis is provided.
  • DFL exponent alpha = not stated
    Equation (21) defines alpha as controlling loss sensitivity; its value and sensitivity are never reported.
  • ALC importance-freeze epoch = after a 40-epoch warmup
    The exact epoch at which Fisher importance is computed is left unspecified; a design choice that could affect results.
assumptions (4)
  • domain assumption Gradient magnitude approximates parameter importance in the ALC layer.
    Eq. (1) defines Fisher importance as squared gradient of the loss; this is borrowed from EWC and is an approximation for the stated purpose.
  • domain assumption DTCWT and NSCT decompositions of 2D slices adequately represent 3D volumetric MRI structure.
    The FDD module is applied per 2D slice (Section III-D) despite the 3D framing; inter-slice coherence is handled downstream but not in the decomposition.
  • domain assumption The paired outputs are comparable enough that using one branch as a pseudo-label for the other is beneficial.
    Eq. (18) defines the unsupervised consistency loss; the assumption that mutual pseudo-labeling improves both branches is not tested without it.
  • domain assumption The 'pyrexc' and 'cd' filter banks deliver the directional selectivity claimed.
    Section III-D invokes these classical filter designs without verifying their behavior on MRI inputs.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective." pith.science (2026). https://pith.science/paper/USGOO2BQ

@misc{pith2026250610142,
  author       = {Pith},
  title        = {Pith review of: Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/USGOO2BQ}},
  note         = {Machine review of arXiv:2506.10142}
}
read the original abstract

Precise segmentation of brain tumors, particularly contrast-enhancing regions visible in post-contrast MRI (areas highlighted by contrast agent injection), is crucial for accurate clinical diagnosis and treatment planning but remains challenging. However, current methods exhibit notable performance degradation in segmenting these enhancing brain tumor areas, largely due to insufficient consideration of MRI-specific tumor features such as complex textures and directional variations. To address this, we propose the Harmonized Frequency Fusion Network (HFF-Net), which rethinks brain tumor segmentation from a frequency-domain perspective. To comprehensively characterize tumor regions, we develop a Frequency Domain Decomposition (FDD) module that separates MRI images into low-frequency components, capturing smooth tumor contours and high-frequency components, highlighting detailed textures and directional edges. To further enhance sensitivity to tumor boundaries, we introduce an Adaptive Laplacian Convolution (ALC) module that adaptively emphasizes critical high-frequency details using dynamically updated convolution kernels. To effectively fuse tumor features across multiple scales, we design a Frequency Domain Cross-Attention (FDCA) integrating semantic, positional, and slice-specific information. We further validate and interpret frequency-domain improvements through visualization, theoretical reasoning, and experimental analyses. Extensive experiments on four public datasets demonstrate that HFF-Net achieves an average relative improvement of 4.48\% (ranging from 2.39\% to 7.72\%) in the mean Dice scores across the three major subregions, and an average relative improvement of 7.33% (ranging from 5.96% to 8.64%) in the segmentation of contrast-enhancing tumor regions, while maintaining favorable computational efficiency and clinical applicability. Code: https://github.com/VinyehShaw/HFF.

Figures

Figures reproduced from arXiv: 2506.10142 by the authors.

Figure 1
Figure 1. (a) Segmentation of all tumor regions in a complex brain glioma case. (b) Multi-directional HF (Hi ) and LF (L) input (FLAIR) example in our proposed method in this case. (c) Comparison of the previous work’s degraded segmentation performance in contrast-enhancing tu￾mor region with our approach in this case. Red arrows show where our predictions closely match the ground truth. I. INTRODUCTION B RAIN tumors, particu… view at source ↗
Figure 2
Figure 2. (a) Architecture of our HFF-Net: A multimodal dual-branch network decomposing and integrating multi-directional HF and LF MRI features with three components: ALC, FDCA, and FDD. It uses Lunsup for output consistency between branches and L H,L sup to align each branch’s main and side outputs with ground truth. (b) Our ALC uses elastic weight consolidation to dynamically update weights, maintaining HF filtering functi… view at source ↗
Figure 3
Figure 3. Feature maps between PANet [86] and HFF-Net. Conventional methods often overlook critical frequency-specific cues for brain tumor segmentation. In contrast, HFF-Net decouples frequency components to preserve low-frequency structural integrity while multi-directionally en￾hancing high-frequency tumor boundary details and texture granularity. These parameters contribute to modeling a low-rank Gaussian distribution NL(… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Segmentation results on the LiTS and LA datasets to show the generalizability of our method across different medical imaging modalities. In the LiTS dataset (upper row), red masks denote the liver and green masks represent the lesion region; in the LA dataset (lower ro…
Figure 5
Figure 5. Figure 5: Segmentation performance comparison on the BraTS2019 training dataset. All cases feature structurally complex brain tumors with pronounced spatial heterogeneity. Red masks denote necrotic tumor core (NCR/NET), yellow masks denote enhancing tumors (ET), and green masks …
Figure 6
Figure 6. Figure 6: Grad-CAM visualization of different models on BraTS2020 train￾ing samples for ET, WT, and TC. Red indicates high attention, blue low attention. Compared to baselines, HFF-Net variants show progressively sharper and more focused responses. The full model achieves the be…
Figure 7
Figure 7. Figure 7: Visualization of the frequency representations before and after DTCWT-based low-frequency decomposition and NSCT-based high￾frequency fusion. The 3D amplitude plots illustrate how the frequency components are affected by these decomposition and fusion steps. The freque…
Figure 8
Figure 8. Figure 8: Visual comparison of segmentation results on axial, sagittal, coronal, and 3D views from the BraTS2023-MEN dataset. Red masks denote non-enhancing tumor core (NET), green masks represent surrounding non-enhancing FLAIR hyperintensity (SNFH), and blue masks denote enhan…
Figure 10
Figure 10. Figure 10: Comparative visualization of trade-offs among methods regard￾ing computational complexity, clinical applicability, and segmentation performance. (a) Depicts computational complexity using parameter size, Dice score, and GFLOPs (bubble size). (b) Illustrates inference …
Figure 11
Figure 11. Figure 11: Visualization of failure cases to show how missing FLAIR modality signals affect segmentation. Red arrows highlight areas of significant deviation from the ground truth. ms), the substantial gain in accuracy justifies the computational trade-off in many clinical conte…
Figure 12
Figure 12. Figure 12: Cross-domain segmentation on four 2D datasets shows that our frequency-decoupled design generalizes across medical imaging modalities by maintaining structural integrity and enhancing fine-grained boundaries. Potential strategies include uncertainty modeling, attentio…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

127 extracted references · 53 canonical work pages

  1. [1]

    Exciting new advances in neuro-oncology: the avenue to a cure for malignant glioma,

    E. G. Van Meir, C. G. Hadjipanayis, A. D. Norden, H.-K. Shu, P. Y . Wen, and J. J. Olson, “Exciting new advances in neuro-oncology: the avenue to a cure for malignant glioma,”CA: a cancer journal for clinicians, vol. 60, no. 3, pp. 166–193, 2010

    2010

  2. [2]

    The multimodal brain tumor image segmentation benchmark (brats),

    B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y . Burren, N. Porz, J. Slotboom, R. Wiestet al., “The multimodal brain tumor image segmentation benchmark (brats),”IEEE transactions on medical imaging, vol. 34, no. 10, pp. 1993–2024, 2014

    1993

  3. [3]

    Brain tumor seg- mentation using convolutional neural networks in mri images,

    S. Pereira, A. Pinto, V . Alves, and C. A. Silva, “Brain tumor seg- mentation using convolutional neural networks in mri images,”IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1240–1251, 2016

    2016

  4. [4]

    Evidence and context of use for contrast enhancement as a surrogate of disease burden and treatment response in malignant glioma,

    B. M. Ellingson, P. Y . Wen, and T. F. Cloughesy, “Evidence and context of use for contrast enhancement as a surrogate of disease burden and treatment response in malignant glioma,”Neuro-oncology, vol. 20, no. 4, pp. 457–471, 2018

    2018

  5. [5]

    Patterns of tumor contrast enhancement predict the prognosis of anaplastic gliomas with idh1 mutation,

    Y . Wang, K. Wang, S. Li, J. Wang, J. Ma, T. Jiang, and J. Dai, “Patterns of tumor contrast enhancement predict the prognosis of anaplastic gliomas with idh1 mutation,”American Journal of Neuroradiology, vol. 36, no. 11, pp. 2023–2029, 2015

    2023

  6. [6]

    Mri features predict p53 status in lower-grade gliomas via a machine-learning approach,

    Y . Li, Z. Qian, K. Xu, K. Wang, X. Fan, S. Li, T. Jiang, X. Liu, and Y . Wang, “Mri features predict p53 status in lower-grade gliomas via a machine-learning approach,”NeuroImage: Clinical, vol. 17, pp. 306–311, 2018

    2018

  7. [7]

    Texture analysis in brain tumor mr imaging,

    A. Kunimatsu, K. Yasaka, H. Akai, H. Sugawara, N. Kunimatsu, and O. Abe, “Texture analysis in brain tumor mr imaging,”Magnetic Resonance in Medical Sciences, vol. 21, no. 1, pp. 95–109, 2022

    2022

  8. [8]

    Texture analysis: a review of neuro- logic mr imaging applications,

    A. Kassner and R. Thornhill, “Texture analysis: a review of neuro- logic mr imaging applications,”American Journal of Neuroradiology, vol. 31, no. 5, pp. 809–816, 2010

    2010

Show all 127 references
  1. [9]

    Classification of brain tumor type and grade using mri texture and shape in a machine learning scheme,

    E. I. Zacharaki, S. Wang, S. Chawla, D. Soo Yoo, R. Wolf, E. R. Mel- hem, and C. Davatzikos, “Classification of brain tumor type and grade using mri texture and shape in a machine learning scheme,”Magnetic Resonance in Medicine: An Official Journal of the International Society...

    2009

  2. [10]

    Differentiating high- grade gliomas from brain metastases at magnetic resonance: the role of texture analysis of the peritumoral zone,

    C. Csutak, P.-A. S, tefan, L. M. Lenghel, C. O. Moros,anu, R.-A. Lupean, L. S, imonca, C. M. Mihu, and A. Lebovici, “Differentiating high- grade gliomas from brain metastases at magnetic resonance: the role of texture analysis of the peritumoral zone,”Brain Sciences, vol. 10, ...

    2020

  3. [11]

    Quantitative metric for mr brain tumour grade classification using sample space density measure of analytic intrinsic mode function representation,

    T. Kaur, B. S. Saini, and S. Gupta, “Quantitative metric for mr brain tumour grade classification using sample space density measure of analytic intrinsic mode function representation,”IET Image Processing, vol. 11, no. 8, pp. 620–632, 2017. AUTHORet al.: PREPARATION OF PAPERS...

    2017

  4. [12]

    Classification and segmentation of brain tumor using texture analysis,

    G. L. Qurat-Ul-Ain, S. B. Kazmi, M. A. Jaffar, and A. M. Mirza, “Classification and segmentation of brain tumor using texture analysis,” Recent advances in artificial intelligence, knowledge engineering and data bases, pp. 147–155, 2010

    2010

  5. [13]

    Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

    F. Davnall, C. S. Yip, G. Ljungqvist, M. Selmi, F. Ng, B. Sanghera, B. Ganeshan, K. A. Miles, G. J. Cook, and V . Goh, “Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?” Insights into imaging, vol. 3, pp. 573–589, 2012

    2012

  6. [14]

    Texture analysis in cerebral gliomas: a review of the literature,

    N. Soni, S. Priya, and G. Bathla, “Texture analysis in cerebral gliomas: a review of the literature,”American Journal of Neuroradiology, vol. 40, no. 6, pp. 928–934, 2019

    2019

  7. [15]

    Assessment of multiphasic contrast-enhanced mr textures in differentiating small renal mass subtypes,

    U. N. Hoang, S. Mojdeh Mirmomen, O. Meirelles, J. Yao, M. Merino, A. Metwalli, W. Marston Linehan, and A. A. Malayeri, “Assessment of multiphasic contrast-enhanced mr textures in differentiating small renal mass subtypes,”Abdominal Radiology, vol. 43, pp. 3400–3409, 2018

    2018

  8. [16]

    Diagnostic performance of texture analysis on mri in grading cerebral gliomas,

    K. Skogen, A. Schulz, J. B. Dormagen, B. Ganeshan, E. Helseth, and A. Server, “Diagnostic performance of texture analysis on mri in grading cerebral gliomas,”European journal of radiology, vol. 85, no. 4, pp. 824–829, 2016

    2016

  9. [17]

    Characterizing brain tumor regions using texture analysis in magnetic resonance imaging,

    Y . Yu, X. Wu, J. Chen, G. Cheng, X. Zhang, C. Wan, J. Hu, S. Miao, Y . Yin, Z. Wanget al., “Characterizing brain tumor regions using texture analysis in magnetic resonance imaging,”Frontiers in Neuroscience, vol. 15, p. 634926, 2021

    2021

  10. [18]

    A survey on deep learning in medical image analysis,

    G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. Van Der Laak, B. Van Ginneken, and C. I. S´anchez, “A survey on deep learning in medical image analysis,” Medical image analysis, vol. 42, pp. 60–88, 2017

    2017

  11. [19]

    Fda: Fourier domain adaptation for semantic segmentation,

    Y . Yang and S. Soatto, “Fda: Fourier domain adaptation for semantic segmentation,” inProceedings of the IEEE/CVF conference on com- puter vision and pattern recognition, 2020, pp. 4085–4095

    2020

  12. [20]

    Deep learning based brain tumor segmentation: a survey,

    Z. Liu, L. Tong, L. Chen, Z. Jiang, F. Zhou, Q. Zhang, X. Zhang, Y . Jin, and H. Zhou, “Deep learning based brain tumor segmentation: a survey,”Complex & intelligent systems, vol. 9, no. 1, pp. 1001–1026, 2023

    2023

  13. [21]

    Brain tumor target volume determination for radiation treatment planning through automated mri segmentation,

    G. P. Mazzara, R. P. Velthuizen, J. L. Pearlman, H. M. Greenberg, and H. Wagner, “Brain tumor target volume determination for radiation treatment planning through automated mri segmentation,”International Journal of Radiation Oncology* Biology* Physics, vol. 59, no. 1, pp. 300...

    2004

  14. [22]

    Baseline pretreatment contrast enhancing tumor volume including cen- tral necrosis is a prognostic factor in recurrent glioblastoma: evidence from single and multicenter trials,

    B. M. Ellingson, R. J. Harris, D. C. Woodworth, K. Leu, O. Zaw, W. P. Mason, S. Sahebjam, L. E. Abrey, D. T. Aftab, G. M. Schwabet al., “Baseline pretreatment contrast enhancing tumor volume including cen- tral necrosis is a prognostic factor in recurrent glioblastoma: evidenc...

    2017

  15. [23]

    Com- parison of wavelet transformations to enhance convolutional neural network performance in brain tumor segmentation,

    M. Hajiabadi, B. Alizadeh Savareh, H. Emami, and A. Bashiri, “Com- parison of wavelet transformations to enhance convolutional neural network performance in brain tumor segmentation,”BMC medical informatics and decision making, vol. 21, pp. 1–11, 2021

    2021

  16. [24]

    Medical image segmentation based on frequency domain decomposition svd linear attention,

    L. Qiong, L. Chaofan, T. Jinnan, C. Liping, and S. Jianxiang, “Medical image segmentation based on frequency domain decomposition svd linear attention,”Scientific Reports, vol. 15, no. 1, p. 2833, 2025

    2025

  17. [25]

    Prior wavelet knowledge for multi-modal medical image segmentation using a lightweight neural network with attention guided features,

    V . K. Singh, E. Y . Kalafi, S. Wang, A. Benjamin, M. Asideu, V . Kumar, and A. E. Samir, “Prior wavelet knowledge for multi-modal medical image segmentation using a lightweight neural network with attention guided features,”Expert Systems with Applications, vol. 209, p. 118166, 2022

    2022

  18. [26]

    Spectral u-net: Enhancing med- ical image segmentation via spectral decomposition,

    Y . Peng, M. Sonka, and D. Z. Chen, “Spectral u-net: Enhancing med- ical image segmentation via spectral decomposition,”arXiv preprint arXiv:2409.09216, 2024

    2024 arXiv

  19. [27]

    Dual-tree complex wavelet pooling and attention-based modified u-net architecture for automated breast thermogram segmentation and classification,

    L. Garia and H. Muthusamy, “Dual-tree complex wavelet pooling and attention-based modified u-net architecture for automated breast thermogram segmentation and classification,”Journal of Imaging In- formatics in Medicine, vol. 38, no. 2, pp. 887–901, 2025

    2025

  20. [28]

    Optimal deep learning architecture for automated segmentation of cysts in oct images using x-let transforms,

    R. Darooei, M. Nazari, R. Kafieh, and H. Rabbani, “Optimal deep learning architecture for automated segmentation of cysts in oct images using x-let transforms,”Diagnostics, vol. 13, no. 12, p. 1994, 2023

    1994

  21. [29]

    Wavelet u-net++ for accurate lung nodule segmentation in ct scans: Improving early detection and diagnosis of lung cancer,

    S. A. Agnes, A. A. Solomon, and K. Karthick, “Wavelet u-net++ for accurate lung nodule segmentation in ct scans: Improving early detection and diagnosis of lung cancer,”Biomedical Signal Processing and Control, vol. 87, p. 105509, 2024

    2024

  22. [30]

    Wranet: wavelet integrated residual attention u-net network for medical image segmentation,

    Y . Zhao, S. Wang, Y . Zhang, S. Qiao, and M. Zhang, “Wranet: wavelet integrated residual attention u-net network for medical image segmentation,”Complex & intelligent systems, vol. 9, no. 6, pp. 6971– 6983, 2023

    2023

  23. [31]

    A dual-tree complex wavelet transform based convolutional neural network for hu- man thyroid medical image segmentation,

    H. Lu, H. Wang, Q. Zhang, D. Won, and S. W. Yoon, “A dual-tree complex wavelet transform based convolutional neural network for hu- man thyroid medical image segmentation,” in2018 IEEE international conference on healthcare informatics (ICHI). IEEE, 2018, pp. 191– 198

    2018

  24. [32]

    Medical im- age fusion based on convolutional neural networks and non-subsampled contourlet transform,

    Z. Wang, X. Li, H. Duan, Y . Su, X. Zhang, and X. Guan, “Medical im- age fusion based on convolutional neural networks and non-subsampled contourlet transform,”Expert Systems with Applications, vol. 171, p. 114574, 2021

    2021

  25. [33]

    A deep transfer learning based architecture for brain tumor classification using mr images,

    B. Badjie and E. D. ¨Ulker, “A deep transfer learning based architecture for brain tumor classification using mr images,”Information Technol- ogy and Control, vol. 51, no. 2, pp. 332–344, 2022

    2022

  26. [34]

    Brain tumor classification using meta-heuristic optimized convolutional neural networks,

    S. Z. Kurdi, M. H. Ali, M. M. Jaber, T. Saba, A. Rehman, and R. Dama ˇseviˇcius, “Brain tumor classification using meta-heuristic optimized convolutional neural networks,”Journal of Personalized Medicine, vol. 13, no. 2, p. 181, 2023

    2023

  27. [35]

    Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm,

    M. A. Khan, A. Khan, M. Alhaisoni, A. Alqahtani, S. Alsubai, M. Al- harbi, N. A. Malik, and R. Dama ˇseviˇcius, “Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm,”International journal of imaging systems ...

    2023

  28. [36]

    An efficient approach for the detection of brain tumor using fuzzy logic and u-net cnn classification,

    S. Maqsood, R. Damasevicius, and F. M. Shah, “An efficient approach for the detection of brain tumor using fuzzy logic and u-net cnn classification,” inComputational Science and Its Applications–ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021,...

    2021

  29. [37]

    Glioma/glioblastoma detection in brain mri using pre-trained deep-learning scheme,

    V . Rajinikanth, S. Kadry, R. Dama ˇseviˇcius, R. A. Sujitha, G. Balaji, and M. A. Mohammed, “Glioma/glioblastoma detection in brain mri using pre-trained deep-learning scheme,” in2022 Third International Conference on Intelligent Computing Instrumentation and Control Technolo...

    2022

  30. [38]

    Dsleepnet: Disentanglement learning for personal attribute-agnostic three-stage sleep classification using wearable sensing data,

    B. Zhai, H. Duan, Y . Guan, H. Phan, and W. L. Woo, “Dsleepnet: Disentanglement learning for personal attribute-agnostic three-stage sleep classification using wearable sensing data,”IEEE Journal of Biomedical and Health Informatics, 2025

    2025

  31. [39]

    Sid-nerf: Few-shot nerf based on scene information distribution,

    Y . Li, F. Wan, and Y . Long, “Sid-nerf: Few-shot nerf based on scene information distribution,” in2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024, pp. 1–6

    2024

  32. [40]

    Depth-aware endo- scopic video inpainting,

    F. X. Zhang, S. Chen, X. Xie, and H. P. Shum, “Depth-aware endo- scopic video inpainting,” inInternational Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2024, pp. 143–153

    2024

  33. [41]

    Rules for expectation: Learning to generate rules via social environment modeling,

    J. Pu, H. Duan, J. Zhao, and Y . Long, “Rules for expectation: Learning to generate rules via social environment modeling,”IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 8, pp. 6874– 6887, 2024

    2024

  34. [42]

    Sentinel- guided zero-shot learning: A collaborative paradigm without real data exposure,

    F. Wan, X. Miao, H. Duan, J. Deng, R. Gao, and Y . Long, “Sentinel- guided zero-shot learning: A collaborative paradigm without real data exposure,”IEEE Transactions on Circuits and Systems for Video Technology, 2024

    2024

  35. [43]

    Rethinking score dis- tilling sampling for 3d editing and generation,

    X. Miao, H. Duan, Y . Long, and J. Han, “Rethinking score dis- tilling sampling for 3d editing and generation,”arXiv preprint arXiv:2505.01888, 2025

    2025 arXiv

  36. [44]

    Laser: Efficient language-guided segmentation in neural radiance fields,

    X. Miao, H. Duan, Y . Bai, T. Shah, J. Song, Y . Long, R. Ranjan, and L. Shao, “Laser: Efficient language-guided segmentation in neural radiance fields,”IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

    2025

  37. [45]

    Dynamic unary convolution in transformers,

    H. Duan, Y . Long, S. Wang, H. Zhang, C. G. Willcocks, and L. Shao, “Dynamic unary convolution in transformers,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 11, pp. 12 747– 12 759, 2023

    2023

  38. [46]

    Parameter efficient fine-tuning for multi-modal generative vision models with m¨obius-inspired transformation,

    H. Duan, S. Shao, B. Zhai, T. Shah, J. Han, and R. Ranjan, “Parameter efficient fine-tuning for multi-modal generative vision models with m¨obius-inspired transformation,”International Journal of Computer Vision, pp. 1–14, 2025

    2025

  39. [47]

    Unified spatial-temporal edge-enhanced graph networks for pedestrian trajec- tory prediction,

    R. Li, T. Qiao, S. Katsigiannis, Z. Zhu, and H. P. Shum, “Unified spatial-temporal edge-enhanced graph networks for pedestrian trajec- tory prediction,”IEEE Transactions on Circuits and Systems for Video Technology, 2025

    2025

  40. [48]

    Bp-sgcn: Be- havioral pseudo-label informed sparse graph convolution network for pedestrian and heterogeneous trajectory prediction,

    R. Li, S. Katsigiannis, T.-K. Kim, and H. P. Shum, “Bp-sgcn: Be- havioral pseudo-label informed sparse graph convolution network for pedestrian and heterogeneous trajectory prediction,”IEEE Transactions on Neural Networks and Learning Systems, 2025

    2025

  41. [49]

    On the design fundamen- tals of diffusion models: A survey,

    Z. Chang, G. A. Koulieris, and H. P. Shum, “On the design fundamen- tals of diffusion models: A survey,”arXiv preprint arXiv:2306.04542, 2023. 18 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. XX, NO. XX, XXXX 2025

    2023 arXiv

  42. [50]

    Hint: High- quality inpainting transformer with mask-aware encoding and enhanced attention,

    S. Chen, A. Atapour-Abarghouei, and H. P. Shum, “Hint: High- quality inpainting transformer with mask-aware encoding and enhanced attention,”IEEE Transactions on Multimedia, 2024

    2024

  43. [51]

    3d u-net: learning dense volumetric segmentation from sparse annotation,

    ¨O. C ¸ ic ¸ek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ron- neberger, “3d u-net: learning dense volumetric segmentation from sparse annotation,” inMedical Image Computing and Computer- Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, Oc...

    2016

  44. [52]

    nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,

    F. Isensee, P. F. Jaeger, S. A. Kohl, J. Petersen, and K. H. Maier-Hein, “nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,”Nature methods, vol. 18, no. 2, pp. 203–211, 2021

    2021

  45. [53]

    Unetr: Transformers for 3d medical image segmentation,

    A. Hatamizadeh, Y . Tang, V . Nath, D. Yang, A. Myronenko, B. Land- man, H. R. Roth, and D. Xu, “Unetr: Transformers for 3d medical image segmentation,” inProceedings of the IEEE/CVF winter confer- ence on applications of computer vision, 2022, pp. 574–584

    2022

  46. [54]

    Atten- tion u-net: Learning where to look for the pancreas,

    O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Y . Hammerla, B. Kainzet al., “Atten- tion u-net: Learning where to look for the pancreas,”arXiv preprint arXiv:1804.03999, 2018

    2018 arXiv

  47. [55]

    Road extraction by deep residual u-net,

    Z. Zhang, Q. Liu, and Y . Wang, “Road extraction by deep residual u-net,”IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 5, pp. 749–753, 2018

    2018

  48. [56]

    Unetr++: delving into efficient and accurate 3d medical image segmentation,

    A. M. Shaker, M. Maaz, H. Rasheed, S. Khan, M.-H. Yang, and F. S. Khan, “Unetr++: delving into efficient and accurate 3d medical image segmentation,”IEEE Transactions on Medical Imaging, 2024

    2024

  49. [57]

    Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task,

    Z. Jiang, C. Ding, M. Liu, and D. Tao, “Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task,” inBrainle- sion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction wit...

    2019

  50. [58]

    Sgeresu- net for brain tumor segmentation,

    D. Liu, N. Sheng, T. He, W. Wang, J. Zhang, and J. Zhang, “Sgeresu- net for brain tumor segmentation,”Math. Biosci. Eng, vol. 19, no. 6, pp. 5576–5590, 2022

    2022

  51. [59]

    Modality-adaptive feature interaction for brain tumor segmentation with missing modalities,

    Z. Zhao, H. Yang, and J. Sun, “Modality-adaptive feature interaction for brain tumor segmentation with missing modalities,” inInternational conference on medical image computing and computer-assisted inter- vention. Springer, 2022, pp. 183–192

    2022

  52. [60]

    Transunet: Transformers make strong encoders for medical image segmentation,

    J. Chen, Y . Lu, Q. Yu, X. Luo, E. Adeli, Y . Wang, L. Lu, A. L. Yuille, and Y . Zhou, “Transunet: Transformers make strong encoders for medical image segmentation,”arXiv preprint arXiv:2102.04306, 2021

    2021 arXiv

  53. [61]

    An image is worth 16x16 words: Transformers for image recognition at scale,

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gellyet al., “An image is worth 16x16 words: Transformers for image recognition at scale,”arXiv preprint arXiv:2010.11929, 2020

    2010 arXiv

  54. [62]

    Hnf-netv2 for brain tumor segmentation using multi-modal mr imaging,

    H. Jia, C. Bai, W. Cai, H. Huang, and Y . Xia, “Hnf-netv2 for brain tumor segmentation using multi-modal mr imaging,” inInternational MICCAI Brainlesion Workshop. Springer, 2021, pp. 106–115

    2021

  55. [63]

    Sa-lut-nets: learning sample-adaptive intensity lookup tables for brain tumor segmentation,

    B. Yu, L. Zhou, L. Wang, W. Yang, M. Yang, P. Bourgeat, and J. Fripp, “Sa-lut-nets: learning sample-adaptive intensity lookup tables for brain tumor segmentation,”IEEE Transactions on Medical Imaging, vol. 40, no. 5, pp. 1417–1427, 2021

    2021

  56. [64]

    Medical im- age segmentation via single-source domain generalization with random amplitude spectrum synthesis,

    Q. Qiao, W. Wang, M. Qu, K. Su, B. Jiang, and Q. Guo, “Medical im- age segmentation via single-source domain generalization with random amplitude spectrum synthesis,” inInternational Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2024, pp. 435–445

    2024

  57. [65]

    A review on brain tumor segmentation based on deep learning methods with federated learning techniques,

    M. F. Ahamed, M. M. Hossain, M. Nahiduzzaman, M. R. Islam, M. R. Islam, M. Ahsan, and J. Haider, “A review on brain tumor segmentation based on deep learning methods with federated learning techniques,” Computerized Medical Imaging and Graphics, vol. 110, p. 102313, 2023

    2023

  58. [66]

    Innovative multi-class segmenta- tion for brain tumor mri using noise diffusion probability models and enhancing tumor boundary recognition,

    Z. Liu, C. Ma, W. She, and M. Xie, “Innovative multi-class segmenta- tion for brain tumor mri using noise diffusion probability models and enhancing tumor boundary recognition,”Scientific Reports, vol. 14, no. 1, p. 29576, 2024

    2024

  59. [67]

    Learning in the frequency domain,

    K. Xu, M. Qin, F. Sun, Y . Wang, Y .-K. Chen, and F. Ren, “Learning in the frequency domain,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 1740–1749

    2020

  60. [68]

    Discrete cosin trans- former: Image modeling from frequency domain,

    X. Li, Y . Zhang, J. Yuan, H. Lu, and Y . Zhu, “Discrete cosin trans- former: Image modeling from frequency domain,” inProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 5468–5478

    2023

  61. [69]

    Improving model generalization by on-manifold adversarial augmentation in the frequency domain,

    C. Liu, W. Xiang, Y . He, H. Xue, S. Zheng, and H. Su, “Improving model generalization by on-manifold adversarial augmentation in the frequency domain,”arXiv preprint arXiv:2302.14302, 2023

    2023 arXiv

  62. [70]

    Wavelet-based image tokenizer for vision transformers,

    Z. Zhu and R. Soricut, “Wavelet-based image tokenizer for vision transformers,”arXiv preprint arXiv:2405.18616, 2024

    2024 arXiv

  63. [71]

    Focal frequency loss for image reconstruction and synthesis,

    L. Jiang, B. Dai, W. Wu, and C. C. Loy, “Focal frequency loss for image reconstruction and synthesis,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 13 919– 13 929

    2021

  64. [72]

    Wavelet diffusion models are fast and scalable image generators,

    H. Phung, Q. Dao, and A. Tran, “Wavelet diffusion models are fast and scalable image generators,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 10 199–10 208

    2023

  65. [73]

    Fourier space losses for efficient perceptual image super-resolution,

    D. Fuoli, L. Van Gool, and R. Timofte, “Fourier space losses for efficient perceptual image super-resolution,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 2360–2369

    2021

  66. [74]

    Spectral bayesian uncertainty for image super-resolution,

    T. Liu, J. Cheng, and S. Tan, “Spectral bayesian uncertainty for image super-resolution,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 18 166–18 175

    2023

  67. [75]

    Sea ice change de- tection in sar images based on convolutional-wavelet neural networks,

    F. Gao, X. Wang, Y . Gao, J. Dong, and S. Wang, “Sea ice change de- tection in sar images based on convolutional-wavelet neural networks,” IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 8, pp. 1240– 1244, 2019

    2019

  68. [76]

    Xnet: Wavelet- based low and high frequency fusion networks for fully-and semi- supervised semantic segmentation of biomedical images,

    Y . Zhou, J. Huang, C. Wang, L. Song, and G. Yang, “Xnet: Wavelet- based low and high frequency fusion networks for fully-and semi- supervised semantic segmentation of biomedical images,” inProceed- ings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 21...

    2023

  69. [77]

    Aerial lanenet: Lane-marking semantic segmentation in aerial imagery using wavelet- enhanced cost-sensitive symmetric fully convolutional neural net- works,

    S. M. Azimi, P. Fischer, M. K ¨orner, and P. Reinartz, “Aerial lanenet: Lane-marking semantic segmentation in aerial imagery using wavelet- enhanced cost-sensitive symmetric fully convolutional neural net- works,”IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no....

    2018

  70. [78]

    Structural and statistical texture knowledge distillation and learning for segmentation,

    D. Ji, F. Zhao, H. Lu, F. Wu, and J. Ye, “Structural and statistical texture knowledge distillation and learning for segmentation,”IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

    2025

  71. [79]

    A new contourlet transform with sharp frequency localization,

    Y . Lu and M. N. Do, “A new contourlet transform with sharp frequency localization,” in2006 International Conference on Image Processing. IEEE, 2006, pp. 1629–1632

    2006

  72. [80]

    The nonsubsampled con- tourlet transform: theory, design, and applications,

    A. L. Da Cunha, J. Zhou, and M. N. Do, “The nonsubsampled con- tourlet transform: theory, design, and applications,”IEEE transactions on image processing, vol. 15, no. 10, pp. 3089–3101, 2006

    2006

  73. [81]

    Auto- matic multi-organ segmentation of prostate magnetic resonance images using watershed and nonsubsampled contourlet transform,

    Z. Huang, S. Jiang, Z. Yang, Y . Ding, W. Wang, and Y . Yu, “Auto- matic multi-organ segmentation of prostate magnetic resonance images using watershed and nonsubsampled contourlet transform,”Biomedical Signal Processing and Control, vol. 25, pp. 53–61, 2016

    2016

  74. [82]

    Scheme for unsupervised colour–texture image segmentation using neutrosophic set and non- subsampled contourlet transform,

    A. Heshmati, M. Gholami, and A. Rashno, “Scheme for unsupervised colour–texture image segmentation using neutrosophic set and non- subsampled contourlet transform,”IET Image Processing, vol. 10, no. 6, pp. 464–473, 2016

    2016

  75. [83]

    Brain mr image segmentation by modified active contours and contourlet transform

    P. Nageswara Reddy, C. Mohan Rao, and C. Satyanarayana, “Brain mr image segmentation by modified active contours and contourlet transform.”ICTACT Journal on Image & Video Processing, vol. 8, no. 2, 2017

    2017

  76. [84]

    Edge detection methods and filters used on digital image processing,

    C. I. Gonzalez, P. Melin, J. R. Castro, O. Castillo, C. I. Gonzalez, P. Melin, J. R. Castro, and O. Castillo, “Edge detection methods and filters used on digital image processing,”Edge detection methods based on generalized type-2 fuzzy logic, pp. 11–16, 2017

    2017

  77. [85]

    A comprehensive survey of continual learning: Theory, method and application,

    L. Wang, X. Zhang, H. Su, and J. Zhu, “A comprehensive survey of continual learning: Theory, method and application,”IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 2024

    2024

  78. [86]

    Prior attention network for multi-lesion segmentation in medical images,

    X. Zhao, P. Zhang, F. Song, C. Ma, G. Fan, Y . Sun, Y . Feng, and G. Zhang, “Prior attention network for multi-lesion segmentation in medical images,”IEEE Transactions on Medical Imaging, vol. 41, no. 12, pp. 3812–3823, 2022

    2022

  79. [87]

    Non-separable bidimensional wavelet bases,

    A. Cohen and I. Daubechies, “Non-separable bidimensional wavelet bases,”Revista Matematica Iberoamericana, vol. 9, no. 1, pp. 51–137, 1993

    1993

  80. [88]

    V-net: Fully convolutional neural networks for volumetric medical image segmentation,

    F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in2016 fourth international conference on 3D vision (3DV). Ieee, 2016, pp. 565–571

    2016

  81. [89]

    The asnr- miccai brain tumor segmentation (brats) challenge 2023: Intracranial meningioma,

    D. LaBella, M. Adewole, M. Alonso-Basanta, T. Altes, S. M. Anwar, U. Baid, T. Bergquist, R. Bhalerao, S. Chen, V . Chunget al., “The asnr- miccai brain tumor segmentation (brats) challenge 2023: Intracranial meningioma,”arXiv preprint arXiv:2305.07642, 2023. AUTHORet al.: PREP...

    2023 arXiv

  82. [90]

    The medical segmentation decathlon,

    M. Antonelli, A. Reinke, S. Bakas, K. Farahani, A. Kopp-Schneider, B. A. Landman, G. Litjens, B. Menze, O. Ronneberger, R. M. Summers et al., “The medical segmentation decathlon,”Nature communications, vol. 13, no. 1, p. 4128, 2022

    2022

  83. [91]

    Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features,

    S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. S. Kirby, J. B. Freymann, K. Farahani, and C. Davatzikos, “Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features,”Scientific data, vol. 4, no. 1, pp. 1–13, 2017

    2017

  84. [92]

    Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge,

    S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, R. T. Shinohara, C. Berger, S. M. Ha, M. Rozyckiet al., “Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge...

    2018 arXiv

  85. [93]

    Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,

    K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE international conference on computer vision, 2015, pp. 1026–1034

    2015

  86. [94]

    Inter-slice context residual learning for 3d medical image segmentation,

    J. Zhang, Y . Xie, Y . Wang, and Y . Xia, “Inter-slice context residual learning for 3d medical image segmentation,”IEEE Transactions on Medical Imaging, vol. 40, no. 2, pp. 661–672, 2020

    2020

  87. [95]

    Transbts: multimodal brain tumor segmentation using transformer, medical image computing and computer assisted intervention-miccai 2021,

    W. Wang, C. Chen, M. Ding, H. Yu, S. Zha, and J. Li, “Transbts: multimodal brain tumor segmentation using transformer, medical image computing and computer assisted intervention-miccai 2021,” inPro- ceedings of the International Conference on Medical Image Computing and Comput...

    2021

  88. [96]

    A robust volumetric transformer for accurate 3d tumor segmentation,

    H. Peiris, M. Hayat, Z. Chen, G. Egan, and M. Harandi, “A robust volumetric transformer for accurate 3d tumor segmentation,” inInterna- tional conference on medical image computing and computer-assisted intervention. Springer, 2022, pp. 162–172

    2022

  89. [97]

    Shape-scale co- awareness network for 3d brain tumor segmentation,

    L. Zhou, Y . Jiang, W. Li, J. Hu, and S. Zheng, “Shape-scale co- awareness network for 3d brain tumor segmentation,”IEEE Transac- tions on Medical Imaging, 2024

    2024

  90. [98]

    Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,

    S. Zheng, J. Lu, H. Zhao, X. Zhu, Z. Luo, Y . Wang, Y . Fu, J. Feng, T. Xiang, P. H. Torret al., “Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2...

    2021

  91. [99]

    Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation,

    Y . Xie, J. Zhang, C. Shen, and Y . Xia, “Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation,” inMedical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 202...

    2021

  92. [100]

    nnformer: Interleaved transformer for volumetric segmentation,

    H.-Y . Zhou, J. Guo, Y . Zhang, L. Yu, L. Wang, and Y . Yu, “nnformer: Interleaved transformer for volumetric segmentation,”arXiv preprint arXiv:2109.03201, 2021

    2021 arXiv

  93. [101]

    Liver vessels segmentation based on 3d residual u-net,

    W. Yu, B. Fang, Y . Liu, M. Gao, S. Zheng, and Y . Wang, “Liver vessels segmentation based on 3d residual u-net,” in2019 IEEE international conference on image processing (ICIP). IEEE, 2019, pp. 250–254

    2019

  94. [102]

    Swin-unet: Unet-like pure transformer for medical image segmenta- tion,

    H. Cao, Y . Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, and M. Wang, “Swin-unet: Unet-like pure transformer for medical image segmenta- tion,” inEuropean conference on computer vision. Springer, 2022, pp. 205–218

    2022

  95. [103]

    Unext: Mlp-based rapid medical image segmentation network,

    J. M. J. Valanarasu and V . M. Patel, “Unext: Mlp-based rapid medical image segmentation network,” inInternational conference on medical image computing and computer-assisted intervention. Springer, 2022, pp. 23–33

    2022

  96. [104]

    Transbtsv2: Wider instead of deeper transformer for medical image segmentation,

    J. Li, W. Wang, C. Chen, T. Zhang, S. Zha, H. Yu, and J. Wang, “Transbtsv2: Wider instead of deeper transformer for medical image segmentation,”arXiv preprint arXiv:2201.12785, vol. 1, 2022

    2022 arXiv

  97. [105]

    Bag of tricks for 3d mri brain tumor segmentation,

    Y .-X. Zhao, Y .-M. Zhang, and C.-L. Liu, “Bag of tricks for 3d mri brain tumor segmentation,” inInternational MICCAI Brainlesion Workshop. Springer, 2019, pp. 210–220

    2019

  98. [106]

    Robust semantic segmentation of brain tumor regions from 3d mris,

    A. Myronenko and A. Hatamizadeh, “Robust semantic segmentation of brain tumor regions from 3d mris,” inInternational MICCAI Brainlesion Workshop. Springer, 2019, pp. 82–89

    2019

  99. [107]

    Tunet: End-to-end hierarchical brain tumor segmentation using cascaded networks,

    M. H. Vu, T. Nyholm, and T. L ¨ofstedt, “Tunet: End-to-end hierarchical brain tumor segmentation using cascaded networks,” inBrainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI...

    2019

  100. [108]

    Kiu-net: Towards accurate segmentation of biomedical images using over-complete representations,

    J. M. J. Valanarasu, V . A. Sindagi, I. Hacihaliloglu, and V . M. Patel, “Kiu-net: Towards accurate segmentation of biomedical images using over-complete representations,” inInternational conference on medical image computing and computer-assisted intervention. Springer, 2020,...

    2020

  101. [109]

    Med- danet: Dynamic architecture network for efficient medical volumetric segmentation,

    W. Wang, C. Chen, J. Wang, S. Zha, Y . Zhang, and J. Li, “Med- danet: Dynamic architecture network for efficient medical volumetric segmentation,” inEuropean Conference on Computer Vision. Springer, 2022, pp. 506–522

    2022

  102. [110]

    E2enet: Dynamic sparse feature fusion for accurate and efficient 3d medical image segmentation,

    B. Wu, Q. Xiao, S. Liu, L. Yin, M. Pechenizkiy, D. C. Mocanu, M. Keulen, and E. Mocanu, “E2enet: Dynamic sparse feature fusion for accurate and efficient 3d medical image segmentation,”Advances in Neural Information Processing Systems, vol. 37, pp. 118 483–118 512, 2024

    2024

  103. [111]

    Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,

    A. Hatamizadeh, V . Nath, Y . Tang, D. Yang, H. R. Roth, and D. Xu, “Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,” inInternational MICCAI brainlesion workshop. Springer, 2021, pp. 272–284

    2021

  104. [112]

    Mednext: transformer-driven scaling of convnets for medical image segmentation,

    S. Roy, G. Koehler, C. Ulrich, M. Baumgartner, J. Petersen, F. Isensee, P. F. Jaeger, and K. H. Maier-Hein, “Mednext: transformer-driven scaling of convnets for medical image segmentation,” inInternational Conference on Medical Image Computing and Computer-Assisted Interventio...

    2023

  105. [113]

    Ckd-transbts: clinical knowledge-driven hybrid transformer with modality-correlated cross-attention for brain tumor segmentation,

    J. Lin, J. Lin, C. Lu, H. Chen, H. Lin, B. Zhao, Z. Shi, B. Qiu, X. Pan, Z. Xuet al., “Ckd-transbts: clinical knowledge-driven hybrid transformer with modality-correlated cross-attention for brain tumor segmentation,”IEEE transactions on medical imaging, vol. 42, no. 8, pp. 24...

    2023

  106. [114]

    Swinunetr-v2: Stronger swin transformers with stagewise convolutions for 3d medical image segmentation,

    Y . He, V . Nath, D. Yang, Y . Tang, A. Myronenko, and D. Xu, “Swinunetr-v2: Stronger swin transformers with stagewise convolutions for 3d medical image segmentation,” inInternational Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2023, pp. 416–426

    2023

  107. [115]

    Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation,

    Z. Xing, T. Ye, Y . Yang, G. Liu, and L. Zhu, “Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation,” in International Conference on Medical Image Computing and Computer- Assisted Intervention. Springer, 2024, pp. 578–588

    2024

  108. [116]

    A global benchmark of algo- rithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging,

    Z. Xiong, Q. Xia, Z. Hu, N. Huang, C. Bian, Y . Zheng, S. Vesal, N. Ravikumar, A. Maier, X. Yanget al., “A global benchmark of algo- rithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging,”Medical image analysis, vol. 67, p. 101832, 2021

    2021

  109. [117]

    Semi-supervised medical image segmentation through dual-task consistency,

    X. Luo, J. Chen, T. Song, and G. Wang, “Semi-supervised medical image segmentation through dual-task consistency,” inProceedings of the AAAI conference on artificial intelligence, vol. 35, no. 10, 2021, pp. 8801–8809

    2021

  110. [118]

    The liver tumor segmentation benchmark (lits),

    P. Bilic, P. Christ, H. B. Li, E. V orontsov, A. Ben-Cohen, G. Kaissis, A. Szeskin, C. Jacobs, G. E. H. Mamani, G. Chartrandet al., “The liver tumor segmentation benchmark (lits),”Medical image analysis, vol. 84, p. 102680, 2023

    2023

  111. [119]

    Rethinking bayesian deep learning methods for semi-supervised volumetric medical image segmentation,

    J. Wang and T. Lukasiewicz, “Rethinking bayesian deep learning methods for semi-supervised volumetric medical image segmentation,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 182–190

    2022

  112. [120]

    Grad-cam: Visual explanations from deep networks via gradient-based localization,

    R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” inProceedings of the IEEE international conference on computer vision, 2017, pp. 618–626

    2017

  113. [121]

    Image quality assessment: from error visibility to structural similarity,

    Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,”IEEE transactions on image processing, vol. 13, no. 4, pp. 600–612, 2004

    2004

  114. [122]

    Visualizing data using t-sne

    L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” Journal of machine learning research, vol. 9, no. 11, 2008

    2008

  115. [123]

    CREMI: Challenge on Circuit Reconstruction from Electron Microscopy Images,

    J. Funke, S. Saalfeld, and A. Cardona, “CREMI: Challenge on Circuit Reconstruction from Electron Microscopy Images,” Workshop on Connectomics, MICCAI 2016, 2016. [Online]. Available: https://cremi.org/data

    2016

  116. [124]

    Gland segmentation in colon histology images: The glas challenge contest,

    K. Sirinukunwattana, J. P. Pluim, H. Chen, X. Qi, P.-A. Heng, Y . B. Guo, L. Y . Wang, B. J. Matuszewski, E. Bruni, U. Sanchezet al., “Gland segmentation in colon histology images: The glas challenge contest,”Medical image analysis, vol. 35, pp. 489–502, 2017

    2017

  117. [125]

    Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic),

    N. Codella, V . Rotemberg, P. Tschandl, M. E. Celebi, S. Dusza, D. Gutman, B. Helba, A. Kalloo, K. Liopyris, M. Marchettiet al., “Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic),”arXiv preprint arXi...

    2018 arXiv

  118. [126]

    Kvasir-seg: A segmented polyp dataset,

    D. Jha, P. H. Smedsrud, M. A. Riegler, P. Halvorsen, T. De Lange, D. Johansen, and H. D. Johansen, “Kvasir-seg: A segmented polyp dataset,” inInternational conference on multimedia modeling. Springer, 2019, pp. 451–462

    2019

  119. [127]

    ´Etude comparative de la distribution florale dans une 20 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. XX, NO. XX, XXXX 2025 portion des alpes et des jura,

    P. Jaccard, “ ´Etude comparative de la distribution florale dans une 20 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. XX, NO. XX, XXXX 2025 portion des alpes et des jura,”Bull Soc Vaudoise Sci Nat, vol. 37, pp. 547–579, 1901

    2025

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.