Pith. sign in

REVIEW 3 major objections 5 minor 61 references

The paper claims that representing chest X-rays as correlation-gated graphs before generative modeling yields synthetic images that are both closer to the true distribution and more diverse than pixel-space GAN outputs, improving downstream

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

A latent graph representation of chest X-rays, with a learned topology, is used to generate structure-preserving synthetic images that improve data augmentation for classification and segmentation.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection Interesting LGR-based augmentation idea with consistent gains, but a missing gradient path for the binary topology network makes the core trainability claim unverified as written. the 3 major comments →

arxiv 2508.15920 v1 pith:2DJEOZU6 submitted 2025-08-21 eess.IV

Structure-Preserving Medical Image Generation from a Latent Graph Representation

Kevin Arias , Edwin Vargas , Kumar Vijay Mishra , Antonio Ortega , Henry Arguello This is my paper
classification eess.IV
keywords chest X-raylatent graph representationgenerative adversarial networksdata augmentationgraph convolutional networksimage synthesismedical imagingstructural priors
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

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 representing chest X-ray images as graphs whose vertices are visual features of image patches and whose edges are correlation values gated by a learned binary topology. It argues that this latent graph representation captures anatomy-like structure, and that a generative adversarial model trained on these graphs, rather than on raw pixels, can synthesize new chest X-rays that stay close to the real distribution while being farther from existing training images than conventional GAN outputs. Used as data augmentation, the synthetic images push pneumonia classification accuracy up by 3% and lung segmentation DICE up by 2% over baseline networks. The same graphs also serve directly as inputs to graph-convolutional classifiers, improving on earlier latent-graph constructions in the paper's comparisons.

Core claim

The paper's claim: a generative model for chest X-rays should sample a latent graph rather than raw pixels. Each image is divided into patches; vision-transformer key tokens form vertex signals, and edge weights are the correlation of these features multiplied by a binary topology output by a CNN. The topology is learned self-supervised with a graph-convolutional image decoder and sparsity penalties that favor the lung/heart foreground. A GAN is then trained on these graphs and decoded back to X-rays. Reported result: graph-based ACGAN reaches FID 17.56 versus 50.12 for image-space ACGAN; 1,000 graph-generated augmentations raise classification accuracy from 87.12% to 95.12% and segmentation

What carries the argument

Latent graph representation (LGR): an image becomes an undirected graph P=(F,W) whose vertices are image patches. The vertex signals F are vision-transformer key tokens; edge weights are W=RΘ(C)∘C, where C is the normalized correlation of the key tokens and RΘ is a CNN emitting a binary selection matrix that keeps or removes edges. A graph-convolutional decoder with learned upsampling turns the graph back into the image. Sparsity regularization separates foreground from background, with β>α so the graph concentrates on lungs and heart. For generation, an adversarial network learns the distribution of F; each generated F becomes W through the fixed RΘ and is decoded to an X-ray.

Load-bearing premise

The construction rests on the assumption that the patch-level visual features used to build the graph line up across different chest X-rays in a way that corresponds to the same anatomical regions, and that the binary edge-selection network can actually be trained by backpropagation as described; if either fails, the graph does not preserve structure.

What would settle it

Compare the learned graph's edges against an anatomical ground truth: on images with lung/heart segmentation masks, check whether patch pairs connected by the learned topology fall in the same anatomical region more often than a random graph of the same edge density. If the density of within-region edges is at chance level, the structure-preserving claim collapses.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • If the graph captures anatomy, augmentation quality should scale with graph fidelity: increasing the number of generated LGR augmentations improves accuracy and DICE monotonically up to Q=1,000 in the paper's tables.
  • Graph-based generation decouples content structure from pixel style: the learned topology is consistent across datasets and classes, so a graph trained on one X-ray corpus can in principle be reused for another.
  • The reported FID/RMSE trade-off means data augmentation can move away from existing training samples without leaving the data manifold, directly addressing mode collapse and overcorrelation of augmented data.
  • The same LGR doubles as a standalone encoding for graph-convolutional classifiers, and in the paper's comparisons it yields higher accuracy, F1, AUC, sensitivity, and precision than earlier latent-graph constructions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • A direct test the authors did not run: replace the learned binary topology RΘ with a random binary mask of the same edge density; if FID and downstream accuracy barely change, the learned graph, not the graph formalism, is doing the work.
  • The paper does not describe a gradient estimator through the binary output of RΘ; if the network is trained without a straight-through or relaxation trick, the effective learning signal may come mainly through the correlation term and the reconstruction loss, which a reader could verify by inspecting the optimizer dynamics.
  • Because the LGR is modality-agnostic, an extension is to train the graph-to-image decoder on CT or MRI and generate augmentations across modalities, provided patch-level anatomical correspondence holds.
  • The graph could also be coupled to a diffusion model on graph space, replacing the GAN; this would test whether the structure-preserving benefit is tied to adversarial training or to the graph representation itself.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes a latent graph representation (LGR) for chest X-ray images, in which each vertex is a ViT key token of an image patch and edge weights are learned by a binary-output CNN multiplied by a normalized correlation matrix. A GCN decoder reconstructs images from LGRs via a self-supervised objective with foreground-prioritized sparsity regularization. A GAN is then trained to sample LGRs, and the learned decoder maps these sampled LGRs to structure-preserving synthetic images. The authors evaluate the approach as data augmentation for pneumonia classification and lung segmentation, and as a standalone graph representation for GCN-based classifiers. They report FID improvements over GAN baselines and augmentation gains of up to 3% accuracy and 2% DICE.

Significance. If the method is trainable as described, the paper introduces a genuinely novel way to impose structural priors in medical image generation: instead of generating images directly, the GAN operates in a graph-latent space whose topology is learned. The experimental scope is broad (two classification datasets, one segmentation dataset, multiple GCN backbones, several GAN variants), and the FID/RMSE trade-off analysis in Fig. 7 is a useful diagnostic that goes beyond a single metric. The paper does not release code, so the implementation cannot be checked; nevertheless, the qualitative graph-consistency results in Figs. 4 and 6 are a strength and support the plausibility of the learned topology.

major comments (3)
  1. [Section II, Eqs. (1)-(2)] The central trainability issue is unresolved. Eq. (1) defines W = RΘ(C) ◦ C with RΘ a CNN with 'binary output,' and Eq. (2) trains Θ through an L1 loss on RΘ(C). A hard binary output makes the loss piecewise constant in Θ; standard backpropagation cannot update Θ. The manuscript never specifies a differentiable relaxation (e.g., Gumbel-softmax, straight-through estimator, or a sigmoid with binarization only at inference). Without this detail, the learned topology—and all subsequent results in Tables III-VIII—cannot be obtained as described. Please state the exact training-time form of RΘ, the thresholding rule, and the gradient surrogate used.
  2. [Tables V-VIII] The empirical claims are reported without error bars, confidence intervals, or significance tests. The headline gains are up to 3% accuracy and 2% DICE, but many entries differ by fractions of a percent (e.g., GIN 95.87 to 96.65 across augmentation counts; Table VIII 97.52 vs 96.45). Without multiple seeds or statistical testing, it is unclear whether these improvements are above noise. The Q=0 baseline in Table VI (87.12% for VGG-16 on CXR1) is also not discussed; the reader cannot tell whether the task is near-saturated or whether the baseline reflects an unusual preprocessing/class balance.
  3. [Tables VI-VII and Eq. (6)] The FID evaluation is partly circular for the stated claim of 'structure preservation.' The decoder AΩbar is trained to reconstruct the exact training images from their LGRs, so sampling LGRs near the training LGR distribution will inherit the training distribution by construction. The paper should provide a non-circular validation—for example, FID computed on a held-out set not used to train AΩbar, or quantitative anatomical consistency metrics on generated images—to support the claim that the graph representation, rather than the decoder's memorization of training images, is responsible for the low FID.
minor comments (5)
  1. [Eq. (9)] The FID formula has a sign error: the standard expression has +Tr(Σx + Σxhat - 2(ΣxΣxhat)^(1/2)), not −Tr(·). Please correct this, and confirm that the reported FID values were computed with the standard formula.
  2. [Table II and Eq. (1)] Setup 1 defines W = RΘ(C), while Eq. (1) defines W = RΘ(C) ◦ C for all cases. Clarify whether Setup 1 omits the correlation weighting or whether Eq. (1) is only the default formulation.
  3. [Section II, Eq. (2)] The optimization is called self-supervised, but it uses a foreground/background mask derived from an external anatomical segmentation method [52]. Please clarify whether the mask is used only during LGR training and how much supervision this introduces.
  4. [Section V.A] The threshold that produces the 'binary' selection matrix is never specified (0.5? argmax?). Even if a surrogate gradient is added, the binarization threshold must be stated for reproducibility.
  5. [Eq. (5)] The discriminator DΓ(F,W) takes a graph as input, but the architecture is not described. Please specify how the graph (features and adjacency) is fed to DΓ and how the conditional variants (ACGAN, SegAN) are implemented.

Circularity Check

0 steps flagged

No significant circularity: the LGR pipeline's FID gains partly reflect the decoder being fit to the same images, but the central augmentation claims are independently validated on held-out classification and segmentation, and no load-bearing self-citation exists.

full rationale

Walking the derivation chain: Eq. (1) defines the graph weights W = RΘ(C) ◦ C; Eq. (2) jointly optimizes Θ and Ω for reconstruction and sparsity; Eq. (5) trains a GAN on the resulting LGRs; Eq. (6) decodes generated LGRs into images. Nothing in this chain is a claim that reduces to its own input by construction. The decoder AΩ* is indeed fitted to the same training images whose distribution FID measures, so the absolute FID numbers are partly a measure of autoencoder fit rather than an independent test of structure preservation. However, low FID is not forced: the GAN must still match the LGR distribution, and the paper’s central augmentation claims are validated by held-out classification accuracy (Table VI, VGG-16) and held-out segmentation DICE (Table VII, UNet), which are external to the fitted decoder and therefore provide independent content. There are no load-bearing self-citations: the only possibly self-authored reference ([10], with A. Ortega) is a background geometry/transfer-learning citation and is not used to justify the LGR construction, to exclude alternatives, or to import a uniqueness claim. The paper’s binary-output CNN RΘ (Eq. (1)) with no described differentiable relaxation is a genuine implementation/trainability gap—a hard binary output has zero gradient almost everywhere, so the optimization in Eq. (2) is not implementable as written. That is a correctness/implementation risk, not a circularity, and is flagged here only because the review instructions require flagging missing support; it does not raise the circularity score.

Axiom & Free-Parameter Ledger

5 free parameters · 4 axioms · 0 invented entities

The central claims rest on the domain assumption that ViT features and the learned graph topology capture anatomical structure, plus two ad hoc training assumptions (binary mask trainability and the external segmentation mask). No new physical entities are introduced.

free parameters (5)
  • α (sparsity regularization parameter) = 0.025
    Chosen via the PSNR vs. edge-count trade-off in Fig. 5; used for all remaining experiments.
  • β (foreground sparsity parameter) = β = 12α (i.e., 0.3 when α=0.025)
    Fixed ratio to emphasize foreground connections in Eq. (2).
  • Patch size P = 16
    Defines the number of graph vertices (256×256 / 16×16 = 256 patches); chosen by hand.
  • Number of GCLs H = 10
    Depth of the GCN decoder; fixed across experiments.
  • Interpolation neighbors Kint = 12
    Number of nearest known neighbors for inverse-distance upsampling interpolation in Eq. (4).
axioms (4)
  • domain assumption ViT key tokens provide semantically rich, D-dimensional descriptors for image patches that align across chest X-ray images.
    Central to the LGR construction (Section II, Eq. 1); relies on [51] but is not validated independently in this paper.
  • ad hoc to paper A CNN RΘ with binary output can be trained end-to-end with gradient-based optimization.
    RΘ produces sparse binary selection matrices (Eq. 1, Eq. 2); no gradient estimator (e.g., straight-through) is described.
  • domain assumption The foreground/background binary mask from the external segmentation method [52] is accurate for the used chest X-ray datasets.
    Used to define foreground/background correlations and the sparsity regularization in Eq. (2) with β > α.
  • domain assumption A graph built on patch-level ViT features with learned topology is a valid latent space for generative modeling of chest X-rays.
    The GAN in Eq. (5) is trained on LGRs; if the graph space is not representative, generated graphs will not decode to realistic images.

reviewed 2026-08-05 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Structure-Preserving Medical Image Generation from a Latent Graph Representation." pith.science (2026). https://pith.science/paper/2DJEOZU6

@misc{pith2026250815920,
  author       = {Pith},
  title        = {Pith review of: Structure-Preserving Medical Image Generation from a Latent Graph Representation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2DJEOZU6}},
  note         = {Machine review of arXiv:2508.15920}
}
Share X Bluesky LinkedIn Reddit HN
abstract

Supervised learning techniques have proven their efficacy in many applications with abundant data. However, applying these methods to medical imaging is challenging due to the scarcity of data, given the high acquisition costs and intricate data characteristics of those images, thereby limiting the full potential of deep neural networks. To address the lack of data, augmentation techniques leverage geometry, color, and the synthesis ability of generative models (GMs). Despite previous efforts, gaps in the generation process limit the impact of data augmentation to improve understanding of medical images, e.g., the highly structured nature of some domains, such as X-ray images, is ignored. Current GMs rely solely on the network's capacity to blindly synthesize augmentations that preserve semantic relationships of chest X-ray images, such as anatomical restrictions, representative structures, or structural similarities consistent across datasets. In this paper, we introduce a novel GM that leverages the structural resemblance of medical images by learning a latent graph representation (LGR). We design an end-to-end model to learn (i) a LGR that captures the intrinsic structure of X-ray images and (ii) a graph convolutional network (GCN) that reconstructs the X-ray image from the LGR. We employ adversarial training to guide the generator and discriminator models in learning the distribution of the learned LGR. Using the learned GCN, our approach generates structure-preserving synthetic images by mapping generated LGRs to X-ray. Additionally, we evaluate the learned graph representation for other tasks, such as X-ray image classification and segmentation. Numerical experiments demonstrate the efficacy of our approach, increasing performance up to $3\%$ and $2\%$ for classification and segmentation, respectively.

Figures

Figures reproduced from arXiv: 2508.15920 by Antonio Ortega, Edwin Vargas, Henry Arguello, Kevin Arias, Kumar Vijay Mishra.

Figure 1
Figure 1. Figure 1: Overview of the proposed structure-preserving image analysis. Modules annotated with fire symbols are trainable, and those annotated with snowflake [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Foreground and background segmentation: anatomical segmentation [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Conditional graph-based GAN architectures for image diagnostic tasks. a) ACGAN architecture: class-conditioned synthesis is learned to generate [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Graph semantic consistency across images. In the left-most [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Image quality in terms of PSNR and computational complexity [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Graph concordance: For different X-ray images, we sample ten different points in random locations within the lungs (top row) and manually select [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FID vs RMSE: different approaches of data augmentation located [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: X-ray chest image generation: Image reconstruction is performed from the generated graphs to visually demonstrate the realism and diversity of [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

61 extracted references · 55 canonical work pages

  1. [1]

    An overview of deep learning in medical imaging,

    A. Anaya-Isaza, L. Mera-Jim ´enez, and M. Zequera-Diaz, “An overview of deep learning in medical imaging,” Informatics in Medicine Unlocked, vol. 26, p. 100723, 2021

    work page 2021

  2. [2]

    A review of deep learning-based multiple- lesion recognition from medical images: Classification, detection and segmentation,

    H. Jiang, Z. Diao, T. Shi, Y. Zhou, F. Wang, W. Hu, X. Zhu, S. Luo, G. Tong, and Y.-D. Yao, “A review of deep learning-based multiple- lesion recognition from medical images: Classification, detection and segmentation,” Computers in Biology and Medicine , p. 106726, 2023

    work page 2023

  3. [3]

    MedMNIST v2 – A large-scale lightweight benchmark for 2D and 3D biomedical image classification,

    J. Yang, R. Shi, D. Wei, Z. Liu, L. Zhao, B. Ke, H. Pfister, and B. Ni, “MedMNIST v2 – A large-scale lightweight benchmark for 2D and 3D biomedical image classification,” Scientific Data , vol. 10, no. 1, p. 41, 2023

    work page 2023

  4. [4]

    Medical image segmentation using deep learning: A survey,

    R. Wang, T. Lei, R. Cui, B. Zhang, H. Meng, and A. K. Nandi, “Medical image segmentation using deep learning: A survey,”IET Image Processing, vol. 16, no. 5, pp. 1243–1267, 2022

    2022

  5. [5]

    Semi-supervised medical image segmentation via cross teaching between CNN and transformer,

    X. Luo, M. Hu, T. Song, G. Wang, and S. Zhang, “Semi-supervised medical image segmentation via cross teaching between CNN and transformer,” in International Conference on Medical Imaging with Deep Learning, ser. Proceedings of Machine Learning Research, 2022, pp. 820–833

    work page 2022

  6. [6]

    Deep learning models in medical image analysis,

    M. Tsuneki, “Deep learning models in medical image analysis,” Journal of Oral Biosciences , vol. 64, no. 3, pp. 312–320, 2022

    work page 2022

  7. [7]

    Literature review: Efficient deep neural networks tech- niques for medical image analysis,

    M. A. Abdou, “Literature review: Efficient deep neural networks tech- niques for medical image analysis,” Neural Computing and Applications, vol. 34, no. 8, pp. 5791–5812, 2022

    work page 2022

  8. [8]

    A survey on image data augmen- tation for deep learning,

    C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmen- tation for deep learning,” Journal of Big Data , vol. 6, no. 1, pp. 1–48, 2019

    work page 2019

  9. [9]

    Medical image data augmentation: Techniques, compar- isons and interpretations,

    E. Goceri, “Medical image data augmentation: Techniques, compar- isons and interpretations,” Artificial Intelligence Review , vol. 56, p. 12561–12605, 2023

    work page 2023

  10. [10]

    The geometry of self-supervised learning models and its impact on transfer learning,

    R. Cosentino, S. Shekkizhar, M. Soltanolkotabi, S. Avestimehr, and A. Ortega, “The geometry of self-supervised learning models and its impact on transfer learning,” arXiv preprint arXiv:2209.08622, 2022

    Pith/arXiv arXiv 2022

  11. [11]

    Data augmentation for medical imaging: A systematic literature review,

    F. Garcea, A. Serra, F. Lamberti, and L. Morra, “Data augmentation for medical imaging: A systematic literature review,” Computers in Biology and Medicine, p. 106391, 2022

    work page 2022

  12. [12]

    Deep learning ap- proaches for data augmentation in medical imaging: A review,

    A. Kebaili, J. Lapuyade-Lahorgue, and S. Ruan, “Deep learning ap- proaches for data augmentation in medical imaging: A review,” Journal of Imaging, vol. 9, no. 4, p. 81, 2023

    work page 2023

  13. [13]

    Autoencoders and variational autoencoders in medical image analysis,

    J. Ehrhardt and M. Wilms, “Autoencoders and variational autoencoders in medical image analysis,” in Biomedical Image Synthesis and Simula- tion, ser. The MICCAI Society book Series, N. Burgos and D. Svoboda, Eds. Academic Press, 2022, vol. Methods and Applications, pp. 129– 162

    work page 2022

  14. [14]

    Medical image generation using generative adversarial networks: A review,

    N. K. Singh and K. Raza, “Medical image generation using generative adversarial networks: A review,” in Health informatics: A computational Perspective in Healthcare , ser. Studies in Computational Intelligence, R. Patgiri, A. Biswas, and P. Roy, Eds. Springer, 2021, vol. 932, pp. 77–96

    work page 2021

  15. [15]

    Diffusion models for medical image analysis: A comprehensive survey,

    A. Kazerouni, E. K. Aghdam, M. Heidari, R. Azad, M. Fayyaz, I. Haci- haliloglu, and D. Merhof, “Diffusion models for medical image analysis: A comprehensive survey,” arXiv preprint arXiv:2211.07804, 2022

    Pith/arXiv arXiv 2022

  16. [16]

    A review of medical image data augmentation techniques for deep learning applications,

    P. Chlap, H. Min, N. Vandenberg, J. Dowling, L. Holloway, and A. Ha- worth, “A review of medical image data augmentation techniques for deep learning applications,” Journal of Medical Imaging and Radiation Oncology, vol. 65, no. 5, pp. 545–563, 2021

    work page 2021

  17. [17]

    Adaptive augmenta- tion of medical data using independently conditional variational auto- encoders,

    M. Pesteie, P. Abolmaesumi, and R. N. Rohling, “Adaptive augmenta- tion of medical data using independently conditional variational auto- encoders,” IEEE Transactions on Medical Imaging , vol. 38, no. 12, pp. 2807–2820, 2019

    work page 2019

  18. [18]

    FMRI data augmentation via synthesis,

    P. Zhuang, A. G. Schwing, and O. Koyejo, “FMRI data augmentation via synthesis,” in IEEE International Symposium on Biomedical Imaging , 2019, pp. 1783–1787

    work page 2019

  19. [19]

    Data augmentation in high dimensional low sample size setting using a geometry-based variational autoencoder,

    C. Chadebec, E. Thibeau-Sutre, N. Burgos, and S. Allassonni `ere, “Data augmentation in high dimensional low sample size setting using a geometry-based variational autoencoder,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 3, pp. 2879–2896, 2022

    work page 2022

  20. [20]

    Brain lesion synthesis via progressive adversarial variational auto-encoder,

    J. Huo, V. Vakharia, C. Wu, A. Sharan, A. Ko, S. Ourselin, and R. Sparks, “Brain lesion synthesis via progressive adversarial variational auto-encoder,” in International Workshop on Simulation and Synthesis in Medical Imaging , 2022, pp. 101–111

    work page 2022

  21. [21]

    Seeing what a gan cannot generate,

    D. Bau, J.-Y. Zhu, J. Wulff, W. Peebles, H. Strobelt, B. Zhou, and A. Torralba, “Seeing what a gan cannot generate,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 4502–4511

    work page 2019

  22. [22]

    Veegan: Reducing mode collapse in gans using implicit variational learning,

    A. Srivastava, L. Valkov, C. Russell, M. U. Gutmann, and C. Sutton, “Veegan: Reducing mode collapse in gans using implicit variational learning,” Advances in neural information processing systems , vol. 30, 2017

    work page 2017

  23. [23]

    Towards foundation models learned from anatomy in medical imaging via self- supervision,

    M. R. Hosseinzadeh Taher, M. B. Gotway, and J. Liang, “Towards foundation models learned from anatomy in medical imaging via self- supervision,” in MICCAI Workshop on Domain Adaptation and Repre- sentation Transfer. Springer, 2023, pp. 94–104

    work page 2023

  24. [24]

    Nscgcn: A novel deep gcn model to diagnosis covid-19,

    C. Tang, C. Hu, J. Sun, S.-H. Wang, and Y.-D. Zhang, “Nscgcn: A novel deep gcn model to diagnosis covid-19,” Computers in Biology and Medicine, vol. 150, p. 106151, 2022

    work page 2022

  25. [25]

    Cerebrovascular segmentation model based on spatial attention-guided 3D inception U-Net with multi- directional MIPs,

    Y. Liu, H.-S. Kwak, and I.-S. Oh, “Cerebrovascular segmentation model based on spatial attention-guided 3D inception U-Net with multi- directional MIPs,” Applied Sciences, vol. 12, no. 5, p. 2288, 2022

    work page 2022

  26. [26]

    VCNet: Hybrid deep learning model for detection and classification of lung carcinoma using chest radiographs,

    R. Tandon, S. Agrawal, A. Chang, and S. S. Band, “VCNet: Hybrid deep learning model for detection and classification of lung carcinoma using chest radiographs,” Frontiers in Public Health, vol. 10, p. 894920, 2022

    work page 2022

  27. [27]

    Multiclass convolution neural network for classification of COVID-19 CT images,

    S. L. Woan Ching, K. W. Lai, J. H. Chuah, K. Hasikin, A. Khalil, P. Qian, K. Xia, Y. Jiang, Y. Zhang, and S. Dhanalakshmi, “Multiclass convolution neural network for classification of COVID-19 CT images,” Computational Intelligence and Neuroscience , vol. 2022, no. 9167707, p. 15, 2022

    work page 2022

  28. [28]

    Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images,

    C. Srinivas, N. P. KS, M. Zakariah, Y. A. Alothaibi, K. Shaukat, B. Partibane, and H. Awal, “Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images,” Journal of Healthcare Engineering, vol. 2022, no. 3264367, p. 17, 2022

    work page 2022

  29. [29]

    IIMFCBM: Intelligent integrated model for feature extraction and classification of brain tumors using MRI clinical imaging data in IoT-healthcare,

    A. U. Haq, J. P. Li, B. L. Y. Agbley, A. Khan, I. Khan, M. I. Uddin, and S. Khan, “IIMFCBM: Intelligent integrated model for feature extraction and classification of brain tumors using MRI clinical imaging data in IoT-healthcare,” IEEE Journal of Biomedical and Health Informatics , vol. 26, no. 10, pp. 5004–5012, 2022

    work page 2022

  30. [30]

    Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets,

    D. Ueda, A. Yamamoto, N. Onoda, T. Takashima, S. Noda, S. Kashiwagi, T. Morisaki, S. Fukumoto, M. Shiba, M. Morimura, T. Shimono, K. Kageyama, H. Tatekawa, K. Murai, T. Honjo, A. Shimazaki, D. Ka- bata, and Y. Miki, “Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets,” PLoS ...

    work page 2022

  31. [31]

    BI-RADS-based classification of mammographic soft tissue opacities using a deep convolutional neural network,

    A. Sabani, A. Landsmann, P. Hejduk, C. Schmidt, M. Marcon, K. Borkowski, C. Rossi, A. Ciritsis, and A. Boss, “BI-RADS-based classification of mammographic soft tissue opacities using a deep convolutional neural network,”Diagnostics, vol. 12, no. 7, p. 1564, 2022

    work page 2022

  32. [32]

    CNN based fundus images classi- fication for glaucoma identification,

    T. Shyamalee and D. Meedeniya, “CNN based fundus images classi- fication for glaucoma identification,” in International Conference on Advanced Research in Computing , 2022, pp. 200–205

    work page 2022

  33. [33]

    Generative adversarial networks in medical image augmentation: A review,

    Y. Chen, X.-H. Yang, Z. Wei, A. A. Heidari, N. Zheng, Z. Li, H. Chen, H. Hu, Q. Zhou, and Q. Guan, “Generative adversarial networks in medical image augmentation: A review,” Computers in Biology and Medicine, vol. 144, p. 105382, 2022

    work page 2022

  34. [34]

    Synthesizing anonymized and labeled TOF- MRA patches for brain vessel segmentation using generative adversarial networks,

    T. Kossen, P. Subramaniam, V. I. Madai, A. Hennemuth, K. Hildebrand, A. Hilbert, J. Sobesky, M. Livne, I. Galinovic, A. A. Khalil, J. B. Fiebach, and D. Frey, “Synthesizing anonymized and labeled TOF- MRA patches for brain vessel segmentation using generative adversarial networks,” Computers in Biology and Medicine , vol. 131, p. 104254, 2021

    work page 2021

  35. [35]

    MM-GAN: 3D MRI data augmentation for medical image segmentation via generative adversarial networks,

    Y. Sun, P. Yuan, and Y. Sun, “MM-GAN: 3D MRI data augmentation for medical image segmentation via generative adversarial networks,” in IEEE International Conference on Knowledge Graph , 2020, pp. 227– 234

    work page 2020

  36. [36]

    A framework for in-vivo human brain tumor detection using image augmentation and hybrid features,

    M. Jha, R. Gupta, and R. Saxena, “A framework for in-vivo human brain tumor detection using image augmentation and hybrid features,” Health Information Science And Systems , vol. 10, no. 1, p. 23, 2022

    work page 2022

  37. [37]

    Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks,

    Y. Onishi, A. Teramoto, M. Tsujimoto, T. Tsukamoto, K. Saito, H. Toyama, K. Imaizumi, and H. Fujita, “Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks,” International Jour- nal of Computer Assisted Radiology and Surgery , vol. 15, pp. 173–178, 2020

    work page 2020

  38. [38]

    Breast cancer detection using GAN for limited labeled dataset,

    S. D. Desai, S. Giraddi, N. Verma, P. Gupta, and S. Ramya, “Breast cancer detection using GAN for limited labeled dataset,” in IEEE Inter- national Conference on Computational Intelligence and Communication Networks, 2020, pp. 34–39

    work page 2020

  39. [39]

    Leveraging regular fundus images for training UWF fundus diagnosis models via adversarial learning and pseudo-labeling,

    L. Ju, X. Wang, X. Zhao, P. Bonnington, T. Drummond, and Z. Ge, “Leveraging regular fundus images for training UWF fundus diagnosis models via adversarial learning and pseudo-labeling,” IEEE Transactions on Medical Imaging , vol. 40, no. 10, pp. 2911–2925, 2021

    work page 2021

  40. [40]

    Synthetic CT image generation of shape-controlled lung 12 cancer using semi-conditional InfoGAN and its applicability for type classification,

    R. Toda, A. Teramoto, M. Tsujimoto, H. Toyama, K. Imaizumi, K. Saito, and H. Fujita, “Synthetic CT image generation of shape-controlled lung 12 cancer using semi-conditional InfoGAN and its applicability for type classification,” International Journal of Computer Assisted Radiology and Surgery, vol. 16, pp. 241–251, 2021

    work page 2021

  41. [41]

    Mass image synthesis in mammogram with contextual information based on GANs,

    T. Shen, K. Hao, C. Gou, and F.-Y. Wang, “Mass image synthesis in mammogram with contextual information based on GANs,” Computer Methods and Programs in Biomedicine , vol. 202, p. 106019, 2021

    work page 2021

  42. [42]

    Data augmentation of thyroid ultrasound images using generative adversarial network,

    J. Liang and J. Chen, “Data augmentation of thyroid ultrasound images using generative adversarial network,” in IEEE International Ultrasonics Symposium, 2021, pp. 1–4

    work page 2021

  43. [43]

    Brain tumor classi- fication using a combination of variational autoencoders and generative adversarial networks,

    B. Ahmad, J. Sun, Q. You, V. Palade, and Z. Mao, “Brain tumor classi- fication using a combination of variational autoencoders and generative adversarial networks,” Biomedicines, vol. 10, no. 2, p. 223, 2022

    work page 2022

  44. [44]

    Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling,

    C. Biffi, O. Oktay, G. Tarroni, W. Bai, A. De Marvao, G. Doumou, M. Rajchl, R. Bedair, S. Prasad, S. Cook, D. O’Regan, and D. Rueckert, “Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling,” in International Conference on Medical Image Computing and Computer Assisted Intervention , vol. Part II 11, ...

    work page 2018

  45. [45]

    Spot the fake lungs: Generating synthetic medical images using neural diffusion models,

    H. Ali, S. Murad, and Z. Shah, “Spot the fake lungs: Generating synthetic medical images using neural diffusion models,” in Irish Conference on Artificial Intelligence and Cognitive Science , 2022, pp. 32–39

    work page 2022

  46. [46]

    Brain imaging generation with latent diffusion models,

    W. H. Pinaya, P.-D. Tudosiu, J. Dafflon, P. F. Da Costa, V. Fernandez, P. Nachev, S. Ourselin, and M. J. Cardoso, “Brain imaging generation with latent diffusion models,” in MICCAI Workshop on Deep Generative Models, 2022, pp. 117–126

    work page 2022

  47. [47]

    Can segmentation models be trained with fully synthetically generated data?

    V. Fernandez, W. H. L. Pinaya, P. Borges, P.-D. Tudosiu, M. S. Graham, T. Vercauteren, and M. J. Cardoso, “Can segmentation models be trained with fully synthetically generated data?” in International Workshop on Simulation and Synthesis in Medical Imaging , 2022, pp. 79–90

    work page 2022

  48. [48]

    Brain tumor segmentation using synthetic mr images-a comparison of gans and diffusion models,

    M. Usman Akbar, M. Larsson, I. Blystad, and A. Eklund, “Brain tumor segmentation using synthetic mr images-a comparison of gans and diffusion models,” Scientific Data, vol. 11, no. 1, p. 259, 2024

    work page 2024

  49. [49]

    Novel multi-site graph con- volutional network with supervision mechanism for covid-19 diagnosis from x-ray radiographs,

    A. Elazab, M. Abd Elfattah, and Y. Zhang, “Novel multi-site graph con- volutional network with supervision mechanism for covid-19 diagnosis from x-ray radiographs,” Applied Soft Computing , vol. 114, p. 108041, 2022

    work page 2022

  50. [50]

    Frechet inception distance (FID) for evaluating GANs,

    Y. Yu, W. Zhang, and Y. Deng, “Frechet inception distance (FID) for evaluating GANs,” China University of Mining Technology Beijing Graduate School: Beijing, China , 2021

    work page 2021

  51. [51]

    Deep vit features as dense visual descriptors,

    S. Amir, Y. Gandelsman, S. Bagon, and T. Dekel, “Deep vit features as dense visual descriptors,” arXiv preprint arXiv:2112.05814 , vol. 2, no. 3, p. 4, 2021

    Pith/arXiv arXiv 2021

  52. [52]

    Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis,

    N. Gaggion, L. Mansilla, C. Mosquera, D. H. Milone, and E. Ferrante, “Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis,”IEEE Transactions on Medical Imaging , vol. 42, no. 2, pp. 546–556, 2022

    work page 2022

  53. [53]

    Conditional image synthesis with auxiliary classifier gans,

    A. Odena, C. Olah, and J. Shlens, “Conditional image synthesis with auxiliary classifier gans,” in International conference on machine learn- ing. PMLR, 2017, pp. 2642–2651

    work page 2017

  54. [54]

    Segan: Adversarial network with multi-scale l 1 loss for medical image segmentation,

    Y. Xue, T. Xu, H. Zhang, L. R. Long, and X. Huang, “Segan: Adversarial network with multi-scale l 1 loss for medical image segmentation,” Neuroinformatics, vol. 16, pp. 383–392, 2018

    work page 2018

  55. [55]

    Identifying medical diagnoses and treatable diseases by image-based deep learning,

    D. S. Kermany, M. Goldbaum, W. Cai, C. C. Valentim, H. Liang, S. L. Baxter, A. McKeown, G. Yang, X. Wu, F. Yan, J. Dong, M. K. Prasadha, J. Pei, M. Y. L. Ting, J. Zhu, C. Li, S. Hewett, J. Dong, I. Ziyar, A. Shi, R. Zhang, L. Zheng, R. Hou, W. Shi, X. Fu, Y. Duan, V. A. N. Huu, C. Wen, E. D. Zhang, C. L. Zhang, O. Li, X. Wang, M. A. Singer, X. Sun, J. Xu,...

    work page 2018

  56. [56]

    Shiraishi, S

    J. Shiraishi, S. Katsuragawa, J. Ikezoe, T. Matsumoto, T. Kobayashi, K.- i. Komatsu, M. Matsui, H. Fujita, Y. Kodera, and K. Doi, “Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules,” American journal of roentgenology , vol...

    work page 2000

  57. [57]

    Inductive representation learning on large graphs,

    W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” Advances in neural information processing systems, vol. 30, 2017

    2017

  58. [58]

    How powerful are graph neural networks?

    K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” arXiv preprint arXiv:1810.00826, 2018

    Pith/arXiv arXiv 2018

  59. [59]

    Self-attention graph pooling,

    J. Lee, I. Lee, and J. Kang, “Self-attention graph pooling,” in Interna- tional conference on machine learning . pmlr, 2019, pp. 3734–3743

    work page 2019

  60. [60]

    Towards graph pooling by edge contraction,

    F. Diehl, T. Brunner, M. T. Le, and A. Knoll, “Towards graph pooling by edge contraction,” in ICML 2019 workshop on learning and reasoning with graph-structured data, 2019

    work page 2019

  61. [61]

    Graph attention networks,

    P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, Y. Bengio et al., “Graph attention networks,” stat, vol. 1050, no. 20, pp. 10–48 550, 2017

    work page 2017

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.