mlsanityData to 14 Oct 2026

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

arXiv 2411.12350 ↗First posted 19 Nov 2024cs.CV cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

As a technique to alleviate the pressure of data annotation, semi-supervised learning (SSL) has attracted widespread attention. In the specific domain of medical image segmentation, semi-supervised methods (SSMIS) have become a research hotspot due to their ability to reduce the need for large amounts of precisely annotated data. SSMIS focuses on enhancing the model's generalization performance by leveraging a small number of labeled samples and a large number of unlabeled samples. The latest sharpness-aware optimization (SAM) technique, which optimizes the model by reducing the sharpness of the loss function, has shown significant success in SSMIS. However, SAM and its variants may not fully account for the distribution differences between different datasets. To address this issue, we propose a sharpness-aware optimization method based on f-divergence minimization (DiM) for semi-supervised medical image segmentation. This method enhances the model's stability by fine-tuning the sensitivity of model parameters and improves the model's adaptability to different datasets through the introduction of f-divergence. By reducing f-divergence, the DiM method not only improves the performance balance between the source and target datasets but also prevents performance degradation due to overfitting on the source dataset.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 33 of its 48 references that are in the map. First posted on arXiv 19 Nov 2024.

Builds on

33 of 48 references.

References · 33

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  4. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,917
  5. 2020Sharpness-Aware Minimization for Efficiently Improving Generalizationcited by 1,417
  6. 2018Deep Visual Domain Adaptation: A Surveycited by 635
  7. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 586
  8. 2020FDA: Fourier Domain Adaptation for Semantic Segmentationcited by 541
  9. 2021Semi-Supervised Semantic Segmentation with Cross Pseudo Supervisioncited by 463
  10. 2019Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentationcited by 379
  11. 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 352
  12. 2019Semi-supervised Domain Adaptation via Minimax Entropycited by 335
21 more
  1. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  2. 2018Strong-Weak Distribution Alignment for Adaptive Object Detectioncited by 270
  3. 2020Semi-supervised Medical Image Segmentation through Dual-task Consistencycited by 216
  4. 2018A DIRT-T Approach to Unsupervised Domain Adaptationcited by 215
  5. 2022Surrogate Gap Minimization Improves Sharpness-Aware Trainingcited by 172
  6. 2022Towards Understanding Sharpness-Aware Minimizationcited by 146
  7. 2020Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentationcited by 134
  8. 2023Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentationcited by 116
  9. 2021Mutual Consistency Learning for Semi-supervised Medical Image Segmentationcited by 108
  10. 2021Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformercited by 100
  11. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  12. 2022Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentationcited by 93
  13. 2019Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformationscited by 71
  14. 2021Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentationcited by 61
  15. 2020Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosiscited by 60
  16. 2023SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentationcited by 35
  17. 2023ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognitioncited by 31
  18. 2019Semi-Supervised Learning by Augmented Distribution Alignmentcited by 30
  19. 2022LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentationcited by 27
  20. 2024Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentationcited by 14
  21. 2024Challenge Summary U-MedSAM: Uncertainty-aware MedSAM for Medical Image Segmentationcited by 5

Built on by

Not cited yet.