Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation | Paper Citation Record

Observation 7fd564c4-9ea1-4175-a6d7-ab0d2c51f050 · outbound

This paper cites Accessed: 2021-09-06.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Accessed: 2021-09-06

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:25.696220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation da92f133-4d66-4c8c-ab8b-74228ffb257c · outbound

This paper cites Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:14.158714Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2865b3f3-bc29-4718-b06a-4351b4d21d61 · outbound

This paper cites The medical segmentation decathlon.Nature communications, 13(1):1–13, 2022.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation The medical segmentation decathlon.Nature communications, 13(1):1–13, 2022

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:25.581723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation de63e473-08fc-4b8a-b2c3-444775e2559f · outbound

This paper cites Bidirectional copy-paste for semi-supervised medical image segmentation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Bidirectional copy-paste for semi-supervised medical image segmentation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:25.447278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 946b176b-ea81-4a31-a680-14372f76bf7b · outbound

This paper cites Red blood cell image generation for data augmentation using con- ditional generative adversarial networks.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Red blood cell image generation for data augmentation using con- ditional generative adversarial networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:25.248212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d8d74161-0fef-41e4-9267-5c49107ffc46 · outbound

This paper cites Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?IEEE transactions on medical imaging, 37(11):2514–2525, 2018.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?IEEE transactions on medical imaging, 37(11):2514–2525, 2018

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:25.006492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 167d41c9-d039-4502-a9c9-3750fa196c66 · outbound

This paper cites Extracting training data from diffu- sion models.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Extracting training data from diffu- sion models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:24.796617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fe6da545-767e-4071-ba1c-329453557463 · outbound

This paper cites an unresolved cited work.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-06T21:56:24.550312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 13176685-9101-485a-bfe9-87bc1ed11b71 · outbound

This paper cites Semi-supervised task-driven data augmentation for medical image segmentation.Medical Image Analysis, 68:101934, 2021.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Semi-supervised task-driven data augmentation for medical image segmentation.Medical Image Analysis, 68:101934, 2021

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:24.327627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ca4e190a-0060-4f43-9611-cd7853131ac0 · outbound

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

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:15.026552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1b8169b-c0f7-4da9-9d63-57b758a1ae9f · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:15.113543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53f5ed65-6525-4982-bdb1-a84caa02cd6d · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation A simple framework for contrastive learning of visual representations

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:15.177475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c49a700-03f7-4461-ac06-fdcb10e8057e · outbound

This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:15.235656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2293fc89-a668-492a-bb74-9a6d41ec6e11 · outbound

This paper cites Diffusion models beat gans on image synthesis.Advances in neural informa- tion processing systems, 34:8780–8794, 2021.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Diffusion models beat gans on image synthesis.Advances in neural informa- tion processing systems, 34:8780–8794, 2021

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:15.283905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 609f8c43-6aa7-423c-bf71-a1cf90778f60 · outbound

This paper cites Arsdm: Colonoscopy images synthesis with adaptive refinement se- mantic diffusion models.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Arsdm: Colonoscopy images synthesis with adaptive refinement se- mantic diffusion models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:24.120899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 21983463-6f3d-4c5c-8dda-43acde03baab · outbound

This paper cites Can segmentation models be trained with fully synthetically generated data? InInter- national Workshop on Simulation and Synthesis in Medical Imaging, pages 79–90.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Can segmentation models be trained with fully synthetically generated data? InInter- national Workshop on Simulation and Synthesis in Medical Imaging, pages 79–90

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:23.876225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 12c22ca7-5a29-46dd-845f-8f79be60f9d9 · outbound

This paper cites Medgen3d: A deep generative framework for paired 3d image and mask generation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Medgen3d: A deep generative framework for paired 3d image and mask generation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:23.649983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c81b914e-0581-401d-aa8b-81b5f926b7c7 · outbound

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

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Unetr: Transformers for 3d medical image segmentation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:23.450814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 20a68a1d-e3a2-4eb3-a58f-49d96ec5a5d5 · outbound

This paper cites Is synthetic data from generative models ready for image recognition?.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Is synthetic data from generative models ready for image recognition?

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:15.570823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31373d75-e68d-4371-9089-d25bd31b5d94 · outbound

This paper cites Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:15.643391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1086405-69ba-497f-9da3-f14d3ef56a2e · outbound

This paper cites Semi- supervised contrastive learning for label-efficient medical image segmentation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Semi- supervised contrastive learning for label-efficient medical image segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:23.240041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 70348f0c-be8c-4efe-b174-b96128acdc8e · outbound

This paper cites Deep learning approaches for data augmentation in medical imaging: A review.Journal of Imaging, 9(4):81, 2023.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Deep learning approaches for data augmentation in medical imaging: A review.Journal of Imaging, 9(4):81, 2023

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:22.974013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 72969fb5-f599-48d1-9c48-60fb9cef451e · outbound

This paper cites Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:15.889684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca769095-e2a5-48ea-8e1c-fb97afd77bc1 · outbound

This paper cites Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:22.835027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a930dd78-3c25-4250-a740-eac86cd8fba3 · outbound

This paper cites Repaint: Inpainting using denoising diffusion probabilistic models.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Repaint: Inpainting using denoising diffusion probabilistic models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:22.630217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1bc54af4-ddcd-403a-924d-9002e3370bb3 · outbound

This paper cites Semi-supervised medical image segmen- tation via cross teaching between cnn and transformer.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Semi-supervised medical image segmen- tation via cross teaching between cnn and transformer

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:22.442966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 54783a63-a849-49f0-965e-f273373fc83a · outbound

This paper cites Learning data aug- mentation for brain tumor segmentation with coarse-to-fine generative adversarial networks.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Learning data aug- mentation for brain tumor segmentation with coarse-to-fine generative adversarial networks

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:22.224695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 093cec22-6693-4929-b12d-77fa8a303534 · outbound

This paper cites Data augmentation for brain-tumor segmentation: a review.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Data augmentation for brain-tumor segmentation: a review

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:22.068416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1952fe53-5229-468c-8511-27026e9a2a16 · outbound

This paper cites Generative adversarial network based synthesis for supervised medical image segmentation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Generative adversarial network based synthesis for supervised medical image segmentation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:21.905953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e64d876a-c651-43f4-adcf-20e5ede6babd · outbound

This paper cites Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for Pixel-Level Semantic Segmentation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for Pixel-Level Semantic Segmentation

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-06T21:56:18.383434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3ce9386b-47d1-4c7b-8ee2-dce2f6413e8b · outbound

This paper cites Improved denoising diffusion probabilistic models.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Improved denoising diffusion probabilistic models

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:21.731835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 52a52456-15ed-4a22-8c4e-7a52cced9209 · outbound

This paper cites Un- supervised medical image translation with adversarial diffu- sion models.IEEE Transactions on Medical Imaging, 2023.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Un- supervised medical image translation with adversarial diffu- sion models.IEEE Transactions on Medical Imaging, 2023

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:21.585184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 73e57e59-f5fc-4ba0-8d73-eacb67fa6549 · outbound

This paper cites Self-paced contrastive learning for semi-supervised medical image segmentation with meta- labels.Advances in Neural Information Processing Systems, 34:16686–16699, 2021.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Self-paced contrastive learning for semi-supervised medical image segmentation with meta- labels.Advances in Neural Information Processing Systems, 34:16686–16699, 2021

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:21.451348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 818e7734-a01a-43da-8873-897dc3732cd5 · outbound

This paper cites Brain imaging generation with latent diffusion models.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Brain imaging generation with latent diffusion models

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:21.251393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 671a3286-9bfe-4412-8731-2a599221884a · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation High-resolution image synthesis with latent diffusion models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:16.666634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2e6f99e-eeef-4dcc-9580-c7dbc3d32fc6 · outbound

This paper cites Palette: Image-to-image diffusion models.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Palette: Image-to-image diffusion models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:21.035534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 61f7e203-255a-4ddd-b217-457a9a999d0f · outbound

This paper cites A novel data augmentation method using style-based gan for robust pul- monary nodule segmentation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation A novel data augmentation method using style-based gan for robust pul- monary nodule segmentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:20.786748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4a068a7a-6d2c-49e0-a3b4-92c1ac6e3a70 · outbound

This paper cites Medical image synthesis for data augmentation and anonymization using generative adversarial networks.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Medical image synthesis for data augmentation and anonymization using generative adversarial networks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:20.601370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2ffd7e08-74e3-4041-bdec-4858b17a97aa · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:20.403083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5764af69-934c-4613-ad06-4d87a29f67a1 · outbound

This paper cites Diffusion art or digital forgery? investigating data replication in diffusion models.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Diffusion art or digital forgery? investigating data replication in diffusion models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:20.144807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 36099b69-c7e0-4d32-a0b9-49de98c1a1ff · outbound

This paper cites Denoising Diffusion Implicit Models.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Denoising Diffusion Implicit Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:17.122566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83cfe0c2-dda6-42d4-bc3c-6fc6a2102b1b · outbound

This paper cites Effective Data Augmentation With Diffusion Models.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Effective Data Augmentation With Diffusion Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:17.188764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f74c47f0-073f-4ea3-a5a3-2ada92fe87a4 · outbound

This paper cites DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:17.256952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e06852c2-58b1-4eb2-b4a5-afa594bc988c · outbound

This paper cites Exploring smoothness and class-separation for semi-supervised medical image segmentation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Exploring smoothness and class-separation for semi-supervised medical image segmentation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:20.006358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 47af1076-6d24-4a96-a74f-3736951af7cf · outbound

This paper cites Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and do- main adaptation.Medical image analysis, 65:101766, 2020.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and do- main adaptation.Medical image analysis, 65:101766, 2020

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T21:56:17.387517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14058f4d-d253-4d26-8dab-65241c154366 · outbound

This paper cites Revisiting weak-to-strong consistency in semi-supervised semantic segmentation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Revisiting weak-to-strong consistency in semi-supervised semantic segmentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:19.853247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5baa149c-c734-4732-9726-fcc9db725ae0 · outbound

This paper cites Bootstrapping semi-supervised medical image segmentation with anatomical-aware contrastive dis- tillation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Bootstrapping semi-supervised medical image segmentation with anatomical-aware contrastive dis- tillation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:19.730398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 958c34e8-d1b3-467f-96a7-41c6fdc5b6fe · outbound

This paper cites Momentum contrastive voxel-wise representation learning for semi-supervised volumetric medical image seg- mentation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Momentum contrastive voxel-wise representation learning for semi-supervised volumetric medical image seg- mentation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:19.542608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 640df4af-e34a-49fb-8b17-888405f9c8d9 · outbound

This paper cites Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:19.407503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cbd9df96-eb8a-4135-8749-a6970e6d7a90 · outbound

This paper cites Diffusion-based data augmentation for nuclei image segmentation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Diffusion-based data augmentation for nuclei image segmentation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:19.235338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ee942948-6a30-446b-8036-23ae89803fb1 · outbound

This paper cites Positional contrastive learning for volumetric medical image segmentation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Positional contrastive learning for volumetric medical image segmentation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:19.084691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3decf2cb-8d03-4c70-98b3-55a1c6f8089b · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Adding conditional control to text-to-image diffusion models

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:18.952376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 12193b63-10fd-4eb1-b42a-62e0d6ab4177 · outbound

This paper cites Unsupervised feature clustering im- proves contrastive representation learning for medical image segmentation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Unsupervised feature clustering im- proves contrastive representation learning for medical image segmentation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:18.825039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0d3f86ed-568b-405e-9dce-642446657694 · outbound

This paper cites Datasetgan: Efficient labeled data factory with minimal human effort.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Datasetgan: Efficient labeled data factory with minimal human effort

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:18.704469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ab386720-14c2-461a-aca9-cf7e2fb62c93 · outbound

This paper cites Data augmentation using learned transformations for one-shot medical image segmentation.

Inpainting is All You Need: A Diffusion-based Augmentation Method for Semi-supervised Medical Image Segmentation Data augmentation using learned transformations for one-shot medical image segmentation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:56:18.565373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers