Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective | Paper Citation Record

Observation 304f034f-919f-40b7-b7a3-910005448904 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Exciting new advances in neuro-oncology: the avenue to a cure for malignant glioma,

Reference 1

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This paper cites The multimodal brain tumor image segmentation benchmark (brats),.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective The multimodal brain tumor image segmentation benchmark (brats),

Reference 2

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Observation 42d79106-1f26-47d5-9a70-b6b364bf3ec2 · outbound

This paper cites Brain tumor seg- mentation using convolutional neural networks in mri images,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Brain tumor seg- mentation using convolutional neural networks in mri images,

Reference 3

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Evidence and context of use for contrast enhancement as a surrogate of disease burden and treatment response in malignant glioma,

Reference 4

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This paper cites Patterns of tumor contrast enhancement predict the prognosis of anaplastic gliomas with idh1 mutation,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Patterns of tumor contrast enhancement predict the prognosis of anaplastic gliomas with idh1 mutation,

Reference 5

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Mri features predict p53 status in lower-grade gliomas via a machine-learning approach,

Reference 6

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Observation ef18dd40-a41b-4548-a4d5-12ce8546fafe · outbound

This paper cites Texture analysis in brain tumor mr imaging,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Texture analysis in brain tumor mr imaging,

Reference 7

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Observation 4b2aacfe-c8c1-4ea6-a83a-29b61dbbe352 · outbound

This paper cites Texture analysis: a review of neuro- logic mr imaging applications,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Texture analysis: a review of neuro- logic mr imaging applications,

Reference 8

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Observation 73109bde-a97a-40bb-94ef-57bab7b1c090 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Classification of brain tumor type and grade using mri texture and shape in a machine learning scheme,

Reference 9

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Observation 2a904df8-7de4-43bc-b16f-1b3cd3db460b · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Differentiating high- grade gliomas from brain metastases at magnetic resonance: the role of texture analysis of the peritumoral zone,

Reference 10

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Observation 0198061d-87a3-4da2-afe7-1d7b354575d8 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Quantitative metric for mr brain tumour grade classification using sample space density measure of analytic intrinsic mode function representation,

Reference 11

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Observation 5023e32e-e3dc-4b6a-b0b2-4103b5159a9a · outbound

This paper cites Classification and segmentation of brain tumor using texture analysis,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Classification and segmentation of brain tumor using texture analysis,

Reference 12

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Observation 67e43ac4-16f0-411c-bb75-ce06111a1cce · outbound

This paper cites Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

Reference 13

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Observation 33f272c5-72a6-4c2a-879d-f37e7e921a61 · outbound

This paper cites Texture analysis in cerebral gliomas: a review of the literature,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Texture analysis in cerebral gliomas: a review of the literature,

Reference 14

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Observation 8b5240b4-a6ca-4255-90de-4b5dd197cb73 · outbound

This paper cites Assessment of multiphasic contrast-enhanced mr textures in differentiating small renal mass subtypes,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Assessment of multiphasic contrast-enhanced mr textures in differentiating small renal mass subtypes,

Reference 15

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Observation 1c5045ae-a979-438d-87e1-7a2e0d33735e · outbound

This paper cites Diagnostic performance of texture analysis on mri in grading cerebral gliomas,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Diagnostic performance of texture analysis on mri in grading cerebral gliomas,

Reference 16

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Observation f883ef69-efdb-4475-b593-52c1111e2d79 · outbound

This paper cites Characterizing brain tumor regions using texture analysis in magnetic resonance imaging,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Characterizing brain tumor regions using texture analysis in magnetic resonance imaging,

Reference 17

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Observation 1d6f3c68-2cb9-4f00-9930-a326b8aa1e11 · outbound

This paper cites A survey on deep learning in medical image analysis,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective A survey on deep learning in medical image analysis,

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Observation 54d27548-1362-46da-8ad2-e9fa99b84745 · outbound

This paper cites Fda: Fourier domain adaptation for semantic segmentation,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Fda: Fourier domain adaptation for semantic segmentation,

Reference 19

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Observation 74dffa3a-b3ca-42d4-9f9a-82a62341c56a · outbound

This paper cites Deep learning based brain tumor segmentation: a survey,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Deep learning based brain tumor segmentation: a survey,

Reference 20

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Observation 1f2c3305-ba6d-4835-87f5-64fa9a52ab99 · outbound

This paper cites Brain tumor target volume determination for radiation treatment planning through automated mri segmentation,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Brain tumor target volume determination for radiation treatment planning through automated mri segmentation,

Reference 21

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Observation d35ab58c-b556-483a-a774-00e48520ca5e · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Baseline pretreatment contrast enhancing tumor volume including cen- tral necrosis is a prognostic factor in recurrent glioblastoma: evidence from single and multicenter trials,

Reference 22

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Observation f3c32911-f29d-4711-9172-51e6fcb982b8 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Com- parison of wavelet transformations to enhance convolutional neural network performance in brain tumor segmentation,

Reference 23

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Observation b9dc4a09-2419-46fb-bc94-50a54725ed3f · outbound

This paper cites Medical image segmentation based on frequency domain decomposition svd linear attention,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Medical image segmentation based on frequency domain decomposition svd linear attention,

Reference 24

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Prior wavelet knowledge for multi-modal medical image segmentation using a lightweight neural network with attention guided features,

Reference 25

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This paper cites Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition

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Observation f428d09d-5566-4167-9167-7c549b234a21 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Dual-tree complex wavelet pooling and attention-based modified u-net architecture for automated breast thermogram segmentation and classification,

Reference 27

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Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Optimal deep learning architecture for automated segmentation of cysts in oct images using x-let transforms,

Reference 28

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Observation 446d9ca8-987e-49cb-b9eb-94218941b428 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Wavelet u-net++ for accurate lung nodule segmentation in ct scans: Improving early detection and diagnosis of lung cancer,

Reference 29

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Observation 543679ab-85bb-4d81-82a0-7921a5845b45 · outbound

This paper cites Wranet: wavelet integrated residual attention u-net network for medical image segmentation,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Wranet: wavelet integrated residual attention u-net network for medical image segmentation,

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Observation 93eb6486-c680-4ca6-a2d0-9baeac72f735 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective A dual-tree complex wavelet transform based convolutional neural network for hu- man thyroid medical image segmentation,

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Observation 4f8a1052-685c-42b0-9c57-596eb9d2abfb · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Medical im- age fusion based on convolutional neural networks and non-subsampled contourlet transform,

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Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective A deep transfer learning based architecture for brain tumor classification using mr images,

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Observation 694511e5-b196-4c6e-8121-85767cfa5175 · outbound

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Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Brain tumor classification using meta-heuristic optimized convolutional neural networks,

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Observation 5bdd4c34-4ee1-4b4a-baa8-ae7e7e091871 · outbound

This paper cites Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm,

Reference 35

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Observation 8dd89ba0-31cd-4bc4-8874-e139768c8f18 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective An efficient approach for the detection of brain tumor using fuzzy logic and u-net cnn classification,

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Observation 13432fe8-b2d8-460f-9ec6-08f00502e170 · outbound

This paper cites Glioma/glioblastoma detection in brain mri using pre-trained deep-learning scheme,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Glioma/glioblastoma detection in brain mri using pre-trained deep-learning scheme,

Reference 37

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Dsleepnet: Disentanglement learning for personal attribute-agnostic three-stage sleep classification using wearable sensing data,

Reference 38

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This paper cites Sid-nerf: Few-shot nerf based on scene information distribution,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Sid-nerf: Few-shot nerf based on scene information distribution,

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Observation 379d6823-8ffd-471a-bfb6-7b1d25e1a68a · outbound

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Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Depth-aware endo- scopic video inpainting,

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Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Rules for expectation: Learning to generate rules via social environment modeling,

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Observation 3161d9a1-a420-432c-929a-c9efb5cf7751 · outbound

This paper cites Sentinel- guided zero-shot learning: A collaborative paradigm without real data exposure,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Sentinel- guided zero-shot learning: A collaborative paradigm without real data exposure,

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Observation 96eb1c3e-3e7d-4377-9532-94286c82d06e · outbound

This paper cites Rethinking Score Distilling Sampling for 3D Editing and Generation.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Rethinking Score Distilling Sampling for 3D Editing and Generation

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This paper cites Laser: Efficient language-guided segmentation in neural radiance fields,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Laser: Efficient language-guided segmentation in neural radiance fields,

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This paper cites Dynamic unary convolution in transformers,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Dynamic unary convolution in transformers,

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Parameter efficient fine-tuning for multi-modal generative vision models with m¨obius-inspired transformation,

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Observation e9c3155b-48e8-4ba2-a8ee-640d1efb8fd7 · outbound

This paper cites Unified spatial-temporal edge-enhanced graph networks for pedestrian trajec- tory prediction,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Unified spatial-temporal edge-enhanced graph networks for pedestrian trajec- tory prediction,

Reference 47

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Bp-sgcn: Be- havioral pseudo-label informed sparse graph convolution network for pedestrian and heterogeneous trajectory prediction,

Reference 48

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Observation 1e0de972-0b51-484d-ab9d-ef9a6b8bbbb4 · outbound

This paper cites On the Design Fundamentals of Diffusion Models: A Survey.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective On the Design Fundamentals of Diffusion Models: A Survey

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This paper cites Hint: High- quality inpainting transformer with mask-aware encoding and enhanced attention,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Hint: High- quality inpainting transformer with mask-aware encoding and enhanced attention,

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Observation 471a7f66-ffe2-4bad-9236-961e7ae2cf7c · outbound

This paper cites 3d u-net: learning dense volumetric segmentation from sparse annotation,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective 3d u-net: learning dense volumetric segmentation from sparse annotation,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.362305Z

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,

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Observation 10622fbf-4b88-4ede-a9fe-9757d9967366 · outbound

This paper cites Unetr: Transformers for 3d medical image segmentation,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Unetr: Transformers for 3d medical image segmentation,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.347287Z

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Observation 05e9662b-c3a8-40a2-a70c-86024261da84 · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Attention U-Net: Learning Where to Look for the Pancreas

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Observation 65e8d082-e5f4-40d1-a734-653c573baef3 · outbound

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Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Road extraction by deep residual u-net,

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This paper cites Unetr++: delving into efficient and accurate 3d medical image segmentation,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Unetr++: delving into efficient and accurate 3d medical image segmentation,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.332916Z

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task,

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Observation c0b852b5-2140-48f1-89bd-0e1e2304af4b · outbound

This paper cites Sgeresu- net for brain tumor segmentation,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Sgeresu- net for brain tumor segmentation,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.314405Z

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Observation 34853e17-1ca3-4cfc-a1a7-f0d5190c0050 · outbound

This paper cites Modality-adaptive feature interaction for brain tumor segmentation with missing modalities,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Modality-adaptive feature interaction for brain tumor segmentation with missing modalities,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.305774Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3f203293-4dc0-4b68-b8ab-c6e588a55f1e · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:08.985990Z

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Observation 25c19f64-d482-44cf-83c0-4bbc06c2a857 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:09.047934Z

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Observation 93d82cbe-21a7-431b-8938-20fa96c55339 · outbound

This paper cites Hnf-netv2 for brain tumor segmentation using multi-modal mr imaging,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Hnf-netv2 for brain tumor segmentation using multi-modal mr imaging,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.294512Z

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Observation 118d2909-c6a2-4cc6-a75e-f676d5697ac4 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Sa-lut-nets: learning sample-adaptive intensity lookup tables for brain tumor segmentation,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.285978Z

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Observation 5f8d69e3-f0b8-472e-8d31-7f8982c68eb9 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Medical im- age segmentation via single-source domain generalization with random amplitude spectrum synthesis,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.276450Z

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Observation 5d03e33a-97b5-44b6-a2f4-0f5976ab7799 · outbound

This paper cites A review on brain tumor segmentation based on deep learning methods with federated learning techniques,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective A review on brain tumor segmentation based on deep learning methods with federated learning techniques,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.268225Z

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Observation ab121a0d-7bc7-45ba-8d4f-33831ea23a99 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Innovative multi-class segmenta- tion for brain tumor mri using noise diffusion probability models and enhancing tumor boundary recognition,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.260083Z

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Observation e011c64d-6fd9-4404-80de-a3873b37752b · outbound

This paper cites Learning in the frequency domain,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Learning in the frequency domain,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:09.442320Z

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Observation 85301623-ebb1-4925-98b1-721c6109991a · outbound

This paper cites Discrete cosin trans- former: Image modeling from frequency domain,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Discrete cosin trans- former: Image modeling from frequency domain,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.244734Z

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Observation a19db576-cd9e-4a68-9075-b91c19b33c0a · outbound

This paper cites Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:38:09.686754Z

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Observation edfc23b5-104a-4868-a158-1227fd91294c · outbound

This paper cites Wavelet-Based Image Tokenizer for Vision Transformers.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Wavelet-Based Image Tokenizer for Vision Transformers

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:09.450154Z

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Observation 45fb8f97-67e1-4572-806f-0bbcdf9f82b5 · outbound

This paper cites Focal frequency loss for image reconstruction and synthesis,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Focal frequency loss for image reconstruction and synthesis,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.236260Z

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Observation 991563e6-d17b-497a-8e14-cd9f8b8f839c · outbound

This paper cites Wavelet diffusion models are fast and scalable image generators,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Wavelet diffusion models are fast and scalable image generators,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.226581Z

Source-reported events for the cited work

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Observation 90a3565b-912c-4b0f-a1ee-5775ef8f379b · outbound

This paper cites Fourier space losses for efficient perceptual image super-resolution,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Fourier space losses for efficient perceptual image super-resolution,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:09.457908Z

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Observation 910f2eee-9dfe-4da2-a61c-30479d69a63a · outbound

This paper cites Spectral bayesian uncertainty for image super-resolution,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Spectral bayesian uncertainty for image super-resolution,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.213036Z

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Observation ac7f681c-7a64-4e03-a5fd-a494cb58a7b3 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Sea ice change de- tection in sar images based on convolutional-wavelet neural networks,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.205065Z

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Observation 468bacfe-9d18-445b-b207-55d4738a9dec · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Xnet: Wavelet- based low and high frequency fusion networks for fully-and semi- supervised semantic segmentation of biomedical images,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.196815Z

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Observation da7b5738-d1ba-4f2c-8079-b216d8713ca0 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Aerial lanenet: Lane-marking semantic segmentation in aerial imagery using wavelet- enhanced cost-sensitive symmetric fully convolutional neural net- works,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.188880Z

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Observation e36b5573-7951-4ffb-9ede-97d31dc40171 · outbound

This paper cites Structural and statistical texture knowledge distillation and learning for segmentation,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Structural and statistical texture knowledge distillation and learning for segmentation,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.180069Z

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Observation e32eb825-f0c6-4db4-9bbc-0f6235f5d9aa · outbound

This paper cites A new contourlet transform with sharp frequency localization,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective A new contourlet transform with sharp frequency localization,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.172327Z

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Observation 40941e56-3444-4378-aee3-343a7c44d8fd · outbound

This paper cites The nonsubsampled con- tourlet transform: theory, design, and applications,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective The nonsubsampled con- tourlet transform: theory, design, and applications,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.164461Z

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Observation ffd6bf26-a40c-404d-8c4e-d6a57b549901 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Auto- matic multi-organ segmentation of prostate magnetic resonance images using watershed and nonsubsampled contourlet transform,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.156487Z

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Observation 0a34249e-aa45-41ab-b305-a1c54a126b00 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Scheme for unsupervised colour–texture image segmentation using neutrosophic set and non- subsampled contourlet transform,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.148121Z

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Observation d3074fa8-e586-4cde-a96b-dddd9c33d61b · outbound

This paper cites Brain mr image segmentation by modified active contours and contourlet transform.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Brain mr image segmentation by modified active contours and contourlet transform

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.139237Z

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Observation 2ff2f37e-429d-4301-90ca-f4af52e0bffd · outbound

This paper cites Edge detection methods and filters used on digital image processing,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Edge detection methods and filters used on digital image processing,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.130742Z

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Observation cecf4169-0a5a-4f17-a4ba-be2441e50087 · outbound

This paper cites A comprehensive survey of continual learning: Theory, method and application,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective A comprehensive survey of continual learning: Theory, method and application,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.120825Z

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Observation b8319b73-5f5b-4373-b702-2372675a6e6e · outbound

This paper cites Prior attention network for multi-lesion segmentation in medical images,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Prior attention network for multi-lesion segmentation in medical images,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.111556Z

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Observation 1c1c56f3-e19b-4e51-9de7-1825830d8ea9 · outbound

This paper cites Non-separable bidimensional wavelet bases,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Non-separable bidimensional wavelet bases,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.102227Z

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Observation eec245a3-24e6-471f-b1af-df72fd42d5df · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective V-net: Fully convolutional neural networks for volumetric medical image segmentation,

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:09.497448Z

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Observation 9f9f32e6-8455-415d-b3da-3e1b120ceaf9 · outbound

This paper cites The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:09.499844Z

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Observation 1dc5a5bf-2495-4ca0-9bd8-47d884b56561 · outbound

This paper cites The medical segmentation decathlon,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective The medical segmentation decathlon,

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:09.503079Z

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Observation 476dd9f6-8c1d-4262-aa35-c4ef54c64c1a · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features,

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:09.506009Z

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Observation d62a963b-491d-4a11-a93b-43c6cb8ff5ff · outbound

This paper cites Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:09.508741Z

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Observation 4999f7ea-9360-4529-8e68-5473c93d712d · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:09.511495Z

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Observation ba16d7a8-fb40-4449-a122-84a1f54cee92 · outbound

This paper cites Inter-slice context residual learning for 3d medical image segmentation,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Inter-slice context residual learning for 3d medical image segmentation,

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:09.513940Z

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Observation a20097ad-3a13-4ff1-bea7-597d3a97ae00 · outbound

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

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Transbts: multimodal brain tumor segmentation using transformer, medical image computing and computer assisted intervention-miccai 2021,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.064982Z

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Observation 12e785fb-9b4c-4964-b189-8bde66767e60 · outbound

This paper cites A robust volumetric transformer for accurate 3d tumor segmentation,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective A robust volumetric transformer for accurate 3d tumor segmentation,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.055787Z

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Observation 5c579db0-3d0b-4e71-8fa8-23ce611a5884 · outbound

This paper cites Shape-scale co- awareness network for 3d brain tumor segmentation,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Shape-scale co- awareness network for 3d brain tumor segmentation,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.046904Z

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Observation 3cbef443-aa0f-4518-b3fb-e79e38aea52e · outbound

This paper cites Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.037907Z

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Observation f9825aad-23e8-4f6b-9356-d8596b25aaaa · outbound

This paper cites Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation,.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:38:10.029382Z

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Observation 1d1158a5-e66c-4fbf-8b57-ffbb8cb06ae8 · outbound

This paper cites nnFormer: Interleaved Transformer for Volumetric Segmentation.

Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective nnFormer: Interleaved Transformer for Volumetric Segmentation

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-07T04:38:09.530568Z

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