Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation | Paper Citation Record

Observation d84d5d63-750a-4814-8cd0-b9093aff6c65 · outbound

This paper cites Shape-aware meta-learning for generalizing prostate mri segmentation to unseen domains,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Shape-aware meta-learning for generalizing prostate mri segmentation to unseen domains,

Reference 1

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

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation,

Reference 2

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This paper cites Inf-net: Automatic covid-19 lung infection segmentation from ct images,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Inf-net: Automatic covid-19 lung infection segmentation from ct images,

Reference 3

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This paper cites Transformation-consistent self-ensembling model for semisupervised medical image segmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Transformation-consistent self-ensembling model for semisupervised medical image segmentation,

Reference 4

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This paper cites Ss-tbn: A semi-supervised tri-branch network for covid-19 screening and lesion segmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Ss-tbn: A semi-supervised tri-branch network for covid-19 screening and lesion segmentation,

Reference 5

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This paper cites Momentum contrast for unsupervised visual representation learning,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Momentum contrast for unsupervised visual representation learning,

Reference 6

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Observation c2a2b7c3-ad33-4ec2-9f36-7beb6c311948 · outbound

This paper cites Big data in healthcare: management, analysis and future prospects,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Big data in healthcare: management, analysis and future prospects,

Reference 7

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This paper cites Opportunities and challenges in using real-world data for health care,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Opportunities and challenges in using real-world data for health care,

Reference 8

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Observation b2628b7f-3347-4d26-9e51-3c83983a2577 · outbound

This paper cites Semi-supervised learning by aug- mented distribution alignment,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Semi-supervised learning by aug- mented distribution alignment,

Reference 9

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Observation f11afdf5-a639-417f-bb95-c967ca3d6f93 · outbound

This paper cites Domain adaptation for medical image analysis: a survey,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Domain adaptation for medical image analysis: a survey,

Reference 10

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Observation 50a72c9a-c43c-4ca7-8f97-fa4284dad871 · outbound

This paper cites Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation,

Reference 11

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Observation 2e5fbf67-6927-44f2-9588-36447a5b0826 · outbound

This paper cites From source to target and back: symmetric bi-directional adaptive gan,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation From source to target and back: symmetric bi-directional adaptive gan,

Reference 12

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Observation f5f8aa59-988d-42f0-ae9d-153f26d4bf7a · outbound

This paper cites Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss,

Reference 13

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Observation 13c4a3cf-f76f-4f0c-b512-903a009af5f5 · outbound

This paper cites Ecacl: A holistic framework for semi-supervised domain adaptation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Ecacl: A holistic framework for semi-supervised domain adaptation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:42.515937Z

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Observation 11cb3ebe-562f-4334-baab-96e69f18329c · outbound

This paper cites Attract, perturb, and explore: Learning a feature alignment network for semi-supervised domain adaptation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Attract, perturb, and explore: Learning a feature alignment network for semi-supervised domain adaptation,

Reference 15

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Observation 7dc1b850-7180-4608-9bc9-6ffab44b3372 · outbound

This paper cites Multi-level Consistency Learning for Semi-supervised Domain Adaptation.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Multi-level Consistency Learning for Semi-supervised Domain Adaptation

Reference 16

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This paper cites Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Cutmix: Reg- ularization strategy to train strong classifiers with localizable features,

Reference 17

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Observation fc823396-c99d-4228-9f2d-723230d0985c · outbound

This paper cites Separated con- trastive learning for organ-at-risk and gross-tumor-volume segmentation with limited annotation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Separated con- trastive learning for organ-at-risk and gross-tumor-volume segmentation with limited annotation,

Reference 18

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Observation 4591cb03-9690-4807-a2d3-2b3f76859aa7 · outbound

This paper cites Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space,

Reference 19

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Observation cec7209b-63d9-4bd2-982b-97db1ae04691 · outbound

This paper cites Constructing and exploring intermediate domains in mixed domain semi-supervised medical image segmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Constructing and exploring intermediate domains in mixed domain semi-supervised medical image segmentation,

Reference 20

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Observation f28a9526-caba-4c73-8374-d3b401d8e2d0 · outbound

This paper cites Challenges and methodologies of fully automatic whole heart segmentation: a review,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Challenges and methodologies of fully automatic whole heart segmentation: a review,

Reference 21

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Observation 43d336ea-86dc-4a9c-a123-362a77cc65ce · outbound

This paper cites Shape-aware semi-supervised 3d semantic segmentation for medical images,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Shape-aware semi-supervised 3d semantic segmentation for medical images,

Reference 22

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Observation 5990abc5-4547-4570-b04c-7fc906848d83 · outbound

This paper cites Semi-supervised medical image segmentation through dual-task consistency,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Semi-supervised medical image segmentation through dual-task consistency,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:41.673774Z

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Observation 58a90c6f-e9f8-469e-aa46-8c77dc3a8e7e · outbound

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

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Exploring smoothness and class-separation for semi-supervised medical image segmentation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:41.548913Z

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Observation 1e9797aa-c622-40b7-b0d0-6ff07001bb24 · outbound

This paper cites Caussl: Causality- inspired semi-supervised learning for medical image segmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Caussl: Causality- inspired semi-supervised learning for medical image segmentation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:41.429285Z

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Observation e99895da-4bc0-4674-bece-057aacc86b9f · outbound

This paper cites Adaptive bidirectional displace- ment for semi-supervised medical image segmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Adaptive bidirectional displace- ment for semi-supervised medical image segmentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:41.306337Z

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Observation d7d40978-960a-41a0-af90-2319f0e2a115 · outbound

This paper cites Segment anything,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Segment anything,

Reference 27

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Observation eca55203-6a50-45f1-bdc5-a14ef2aab60f · outbound

This paper cites Customized segment anything model for medical image segmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Customized segment anything model for medical image segmentation,

Reference 28

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Observation 7a962c6b-660b-4bf6-aca5-f92fb3cfc5f5 · outbound

This paper cites SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 29

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Observation c1cf171b-ff59-4ebe-9e46-787ecb1074f0 · outbound

This paper cites Segment anything in medical images,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Segment anything in medical images,

Reference 30

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Observation ed2793ca-1f4c-4f2f-8835-e0ddf6b9a12f · outbound

This paper cites Unleashing the potential of sam for medical adaptation via hierarchical decoding,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Unleashing the potential of sam for medical adaptation via hierarchical decoding,

Reference 31

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Observation b20ffddc-a482-4eb7-b4bb-4f0c143ec0b6 · outbound

This paper cites Adversar- ial image synthesis for unpaired multi-modal cardiac data,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Adversar- ial image synthesis for unpaired multi-modal cardiac data,

Reference 32

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Observation 4694f5a7-7c8f-415e-b9aa-133e531d88bd · outbound

This paper cites Domain-adversarial training of neural networks,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Domain-adversarial training of neural networks,

Reference 33

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Observation 0bc8fd2f-38f2-49e3-b901-53240a4453eb · outbound

This paper cites Le- uda: Label-efficient unsupervised domain adaptation for medical image segmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Le- uda: Label-efficient unsupervised domain adaptation for medical image segmentation,

Reference 34

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Observation bec0fa88-ad00-4780-aef1-1ec47d4bb58f · outbound

This paper cites Learning to adapt structured output space for semantic segmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Learning to adapt structured output space for semantic segmentation,

Reference 35

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Observation e0d1f956-52f3-4bb6-8276-79049c880221 · outbound

This paper cites Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation,

Reference 36

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Observation 25f2b91e-1fb5-4481-9975-642fca2bcf09 · outbound

This paper cites Col- laborative unsupervised domain adaptation for medical image diagnosis,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Col- laborative unsupervised domain adaptation for medical image diagnosis,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:40.646383Z

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Observation 47cee455-1d04-4d54-9832-0caa7f0a1d46 · outbound

This paper cites De- liberated domain bridging for domain adaptive semantic segmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation De- liberated domain bridging for domain adaptive semantic segmentation,

Reference 38

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Observation ddd5ae13-702d-42c2-9804-636fdea68b9f · outbound

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

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Fda: Fourier domain adaptation for semantic segmentation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:40.455282Z

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This paper cites Tent: Fully test-time adaptation by entropy minimization,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Tent: Fully test-time adaptation by entropy minimization,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:40.370287Z

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Observation 55a8d4f9-1e0d-4a00-979b-84a2c2b6ebc4 · outbound

This paper cites Each test image deserves a specific prompt: Continual test-time adaptation for 2d medical image segmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Each test image deserves a specific prompt: Continual test-time adaptation for 2d medical image segmentation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:40.260336Z

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Observation fca863fd-d3c2-4b5a-be71-b93edbd22f8e · outbound

This paper cites When Source-Free Domain Adaptation Meets Learning with Noisy Labels.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation When Source-Free Domain Adaptation Meets Learning with Noisy Labels

Reference 42

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Observation 39497bc1-2197-4308-b4df-cbb235e108af · outbound

This paper cites Improving semi-supervised domain adaptation using effective target selection and semantics,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Improving semi-supervised domain adaptation using effective target selection and semantics,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:40.176295Z

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Observation 70b825e1-1c08-4fc8-9586-b4fb4d9a4a09 · outbound

This paper cites Contradictory structure learning for semi-supervised domain adaptation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Contradictory structure learning for semi-supervised domain adaptation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:40.054004Z

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Observation 94971c0a-82ac-418c-9851-177e7b2a209d · outbound

This paper cites Bidirectional adversarial training for semi-supervised domain adaptation.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Bidirectional adversarial training for semi-supervised domain adaptation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:39.915378Z

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Observation 855f516e-6940-4851-9400-abc8be050b9d · outbound

This paper cites Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:24:37.828113Z

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Observation 69c74a13-1e7c-45f8-a477-280957abb9f2 · outbound

This paper cites mixup: Beyond empirical risk minimization,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation mixup: Beyond empirical risk minimization,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:39.794041Z

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Observation dd862dda-d832-479e-8db5-4346efb076ce · outbound

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

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Bidirectional copy-paste for semi-supervised medical image segmentation,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:39.670232Z

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Observation f78027ee-54b6-4158-8b73-b0d04ed43171 · outbound

This paper cites Ucc: Uncertainty guided cross- head co-training for semi-supervised semantic segmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Ucc: Uncertainty guided cross- head co-training for semi-supervised semantic segmentation,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:39.536464Z

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Observation 043bb393-8eca-47ff-bb82-6b9c756dbf4e · outbound

This paper cites Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,

Reference 50

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Observation 09591f05-66b4-4d8d-8c94-9f0c4a403696 · outbound

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

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Fixmatch: Simplifying semi- supervised learning with consistency and confidence,

Reference 51

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This paper cites Fixmatchseg: Fixing fixmatch for semi- supervised semantic segmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Fixmatchseg: Fixing fixmatch for semi- supervised semantic segmentation,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:39.174048Z

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Observation b31c7c54-4399-4d19-8e93-1b6e89c8b67b · outbound

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

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Revisiting weak-to- strong consistency in semi-supervised semantic segmentation,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:39.060482Z

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Observation 86d9f09c-25e5-453b-8fbe-18223f5a1161 · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Bootstrap your own latent-a new approach to self-supervised learning,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:38.888226Z

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Observation 175ab655-519c-4ee2-98dd-f2b1c3f8e669 · outbound

This paper cites Instance credibility inference for few-shot learning,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Instance credibility inference for few-shot learning,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:38.770214Z

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Observation 6616f166-f289-441e-a3cf-794cb944e197 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 56

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Observation ef10cce1-1a06-4434-81f8-8fb8454a090e · outbound

This paper cites Dofe: Domain-oriented feature embedding for generalizable fundus image segmentation on unseen datasets,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Dofe: Domain-oriented feature embedding for generalizable fundus image segmentation on unseen datasets,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:38.645026Z

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Observation eafea23f-ff66-4715-86ef-e80c240cd7f1 · outbound

This paper cites Multi- centre, multi-vendor and multi-disease cardiac segmentation: the m&ms challenge,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Multi- centre, multi-vendor and multi-disease cardiac segmentation: the m&ms challenge,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:38.546288Z

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Observation 37ab5183-afb7-47cb-9d1f-9aecb7eedb97 · outbound

This paper cites Dataset of breast ultrasound images,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Dataset of breast ultrasound images,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:38.408155Z

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Observation 0a407c31-33cb-4222-aad4-8d254f48fd7b · outbound

This paper cites Unsupervised domain adaptation for cardiac segmentation: Towards structure mutual information maximiza- tion,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Unsupervised domain adaptation for cardiac segmentation: Towards structure mutual information maximiza- tion,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:38.267065Z

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Observation fe0be427-d93c-4127-96bd-576584a3be6a · outbound

This paper cites Classmix: Segmentation-based data augmentation for semi-supervised learning,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Classmix: Segmentation-based data augmentation for semi-supervised learning,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:38.183413Z

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Observation af918cdd-a327-4adf-a084-952a472b4e62 · outbound

This paper cites Milking cowmask for semi- supervised image classification,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Milking cowmask for semi- supervised image classification,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:38.079325Z

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Observation db5268a6-c75b-427f-861c-c8550088312e · outbound

This paper cites Fmix: Enhancing mixed sample data augmentation,.

Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation Fmix: Enhancing mixed sample data augmentation,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:24:37.970492Z

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