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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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

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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

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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

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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

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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

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