DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation | Paper Citation Record

Observation 8c04fad3-e50b-4d66-b82c-d5191de433cb · outbound

This paper cites Towards understanding sharpness-aware minimization.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Towards understanding sharpness-aware minimization

Reference 1

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Observation 03fde83a-da5d-455b-adb7-01a0f4841738 · outbound

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Bidirectional copy-paste for semi-supervised medical image segmentation

Reference 2

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Observation ea9f1b58-198c-4ac5-8127-193f25c8441c · outbound

This paper cites Semi-supervised medical image segmentation via learning consistency un- der transformations.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Semi-supervised medical image segmentation via learning consistency un- der transformations

Reference 3

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for med- ical image segmentation

Reference 4

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Observation 015a3d25-2e07-4cfe-8ea9-72c7c14bba40 · outbound

This paper cites Semi-supervised semantic segmentation with cross pseudo supervision.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Semi-supervised semantic segmentation with cross pseudo supervision

Reference 5

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Observation 351b2887-7dce-471c-b6cd-caa6622c24d8 · outbound

This paper cites Image segmentation based on multi-region multi-scale local binary fitting and kullback–leibler divergence.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Image segmentation based on multi-region multi-scale local binary fitting and kullback–leibler divergence

Reference 6

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Observation 040b54a3-b9ca-49c8-9581-81ea6aa10016 · outbound

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Big data in healthcare: management, analysis and future prospects

Reference 7

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Observation b06602c4-7889-4496-ae04-d75b4f855fc2 · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 8

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This paper cites Unsupervised domain adaptation by backpropagation.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Unsupervised domain adaptation by backpropagation

Reference 9

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Observation fe7393f3-5bf9-44a8-a536-20e47b9725f9 · outbound

This paper cites Bias learning, knowl- edge sharing.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Bias learning, knowl- edge sharing

Reference 10

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Observation 18e81f2e-596e-4c4a-bb5b-49cb8682dcee · outbound

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Domain adaptation for medical image analysis: a survey

Reference 11

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Observation ac24d759-0a4a-44e4-8bd2-9e6bef2ec917 · outbound

This paper cites Deep residual learning for image recognition.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Deep residual learning for image recognition

Reference 12

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This paper cites H-denseunet: hybrid densely con- nected unet for liver and tumor segmentation from ct vol- umes.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation H-denseunet: hybrid densely con- nected unet for liver and tumor segmentation from ct vol- umes

Reference 13

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Observation 152a17b7-7d81-4b0d-950f-4bd5988078d8 · outbound

This paper cites Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous fre- quency space.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous fre- quency space

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:42:26.469729Z

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This paper cites Deep unsupervised domain adaptation: A review of recent ad- vances and perspectives.APSIPA Transactions on Signal and Information Processing, 11(1), 2022.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Deep unsupervised domain adaptation: A review of recent ad- vances and perspectives.APSIPA Transactions on Signal and Information Processing, 11(1), 2022

Reference 15

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Observation dc28bea5-9faa-4fff-84dc-2c9ccb8459a5 · outbound

This paper cites Conditional adversarial domain adapta- tion.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Conditional adversarial domain adapta- tion

Reference 16

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This paper cites Unsuper- vised domain adaptation for cardiac segmentation: Towards structure mutual information maximization.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Unsuper- vised domain adaptation for cardiac segmentation: Towards structure mutual information maximization

Reference 17

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Observation a245a91a-1f9f-44e6-8920-2924196f8789 · outbound

This paper cites Uncertainty-aware pseudo-label and consistency for semi- supervised medical image segmentation.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Uncertainty-aware pseudo-label and consistency for semi- supervised medical image segmentation

Reference 18

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Observation 8e65ba69-d69c-4906-bdf5-7d8492c3bce4 · outbound

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Semi-supervised medical image segmentation through dual- task consistency

Reference 19

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Observation ac86ae2b-2665-4b07-a29b-9ca7e2aa84ca · outbound

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Semi-supervised medical image segmen- tation via cross teaching between cnn and transformer

Reference 20

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Constructing and exploring intermediate domains in mixed domain semi-supervised medical image segmenta- tion

Reference 21

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Constructing and exploring intermediate domains in mixed domain semi-supervised medical image segmenta- tion

Reference 22

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Observation 5d9575cc-2ec2-4b7d-8839-353ccb8d1f4a · outbound

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Caussl: Causality-inspired semi-supervised learning for medical image segmentation

Reference 23

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 24

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Observation dd008c28-f988-4a70-8327-06aa697fa72f · outbound

This paper cites Opportunities and challenges in using real-world data for health care.The Jour- nal of Clinical Investigation, 130(2):565–574, 2020.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Opportunities and challenges in using real-world data for health care.The Jour- nal of Clinical Investigation, 130(2):565–574, 2020

Reference 25

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This paper cites Semi-supervised domain adaptation via minimax entropy.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Semi-supervised domain adaptation via minimax entropy

Reference 26

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This paper cites Strong-weak distribution alignment for adaptive ob- ject detection.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Strong-weak distribution alignment for adaptive ob- ject detection

Reference 27

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Observation 139296cd-d7ec-4908-9f9f-8b0411d48845 · outbound

This paper cites Learning from synthetic data: Addressing domain shift for semantic segmentation.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Learning from synthetic data: Addressing domain shift for semantic segmentation

Reference 28

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This paper cites Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation

Reference 29

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Observation 6fe8b787-8981-4199-94e7-9a051447d5bf · outbound

This paper cites A DIRT-T Approach to Unsupervised Domain Adaptation.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation A DIRT-T Approach to Unsupervised Domain Adaptation

Reference 30

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Observation 7973f5a8-73db-417a-aa6c-8828cff9e3a7 · outbound

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 31

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Observation 4946ac6f-b37d-40eb-95b6-b7c5427a06f1 · outbound

This paper cites R ´enyi divergence and kullback-leibler divergence.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation R ´enyi divergence and kullback-leibler divergence

Reference 32

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This paper cites Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning

Reference 33

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Observation 12da36b0-b64b-4229-8a08-90cacb26b8c4 · outbound

This paper cites Deep visual domain adapta- tion: A survey.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Deep visual domain adapta- tion: A survey

Reference 34

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Observation 20c8d8f6-4c2b-4ec6-b655-4458c9023a52 · outbound

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DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Semi-supervised learning by augmented distribution alignment

Reference 35

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Observation f93b2133-1233-452d-9ac7-95eb461b742b · outbound

This paper cites Challenge Summary U-MedSAM: Uncertainty-aware MedSAM for Medical Image Segmentation.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Challenge Summary U-MedSAM: Uncertainty-aware MedSAM for Medical Image Segmentation

Reference 36

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Observation 1e00a0dc-9451-4ff8-878d-7e859d9c7079 · outbound

This paper cites Swinmm: masked multi-view with swin trans- formers for 3d medical image segmentation.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Swinmm: masked multi-view with swin trans- formers for 3d medical image segmentation

Reference 37

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This paper cites A survey of transfer learning.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation A survey of transfer learning

Reference 38

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Observation aacd76fd-bdf9-48b3-a99a-b9b09644a380 · outbound

This paper cites Mutual consistency learning for semi-supervised medical image segmentation.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Mutual consistency learning for semi-supervised medical image segmentation

Reference 39

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Observation e7cdbad4-8da9-4d23-9fe8-98b4c93ed16b · outbound

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Exploring smoothness and class-separation for semi-supervised medical image segmentation

Reference 40

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Fda: Fourier domain adaptation for semantic segmentation

Reference 41

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation

Reference 42

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Observation 27cb4dc7-7966-4f53-8b1a-fff14d289459 · outbound

This paper cites Collab- orative unsupervised domain adaptation for medical image diagnosis.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Collab- orative unsupervised domain adaptation for medical image diagnosis

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:42:26.127161Z

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Observation db2a9373-e817-4099-afd4-d417fea446f2 · outbound

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Le-uda: Label-efficient unsu- pervised domain adaptation for medical image segmentation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:42:26.112902Z

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Observation b6c97f8d-b544-4ef5-8f68-3393dbbeca85 · outbound

This paper cites Imb- sam: A closer look at sharpness-aware minimization in class-imbalanced recognition.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Imb- sam: A closer look at sharpness-aware minimization in class-imbalanced recognition

Reference 45

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Observation 2f2d759d-1c36-425c-96d6-b76c96c160a6 · outbound

This paper cites Unet++: Redesigning skip connections to exploit multiscale features in image segmen- tation.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Unet++: Redesigning skip connections to exploit multiscale features in image segmen- tation

Reference 46

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Observation f29d32b2-6897-4b8d-b239-16ca079bcbf2 · outbound

This paper cites A brief introduction to weakly supervised learning.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation A brief introduction to weakly supervised learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:42:26.068576Z

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Observation 85f107e0-d210-4c65-886f-1c51267e4ef0 · outbound

This paper cites Surrogate Gap Minimization Improves Sharpness-Aware Training.

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Surrogate Gap Minimization Improves Sharpness-Aware Training

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T17:42:25.959460Z

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Observation f2121f8b-54b8-45bf-9471-d37fec9cf21d · outbound

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

DiM: $f$-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation Challenges and methodologies of fully automatic whole heart segmentation: a review

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:42:26.053518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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