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Challenge Summary U-MedSAM: Uncertainty-aware MedSAM for Medical Image Segmentation

Xin Wang, Xiaoyu Liu, Peng Huang, Pu Huang, Shu Hu, Hongtu Zhu

TL;DR

The paper tackles uncertainty in medical image segmentation when using foundation models like MedSAM. It introduces U-MedSAM, which integrates an uncertainty-aware loss that combines pixel, region, and distribution components, and trains with a Sharpness-Aware Minimization optimizer to enhance generalization. A knowledge-distillation step from TinyViT to EfficientViT reduces computational load while preserving performance. On the CVPR24 MedSAM on Laptop challenge, U-MedSAM demonstrates improved Dice scores and robustness across modalities, highlighting gains in accuracy and resilience to uncertain predictions in diverse clinical images.

Abstract

Medical Image Foundation Models have proven to be powerful tools for mask prediction across various datasets. However, accurately assessing the uncertainty of their predictions remains a significant challenge. To address this, we propose a new model, U-MedSAM, which integrates the MedSAM model with an uncertainty-aware loss function and the Sharpness-Aware Minimization (SharpMin) optimizer. The uncertainty-aware loss function automatically combines region-based, distribution-based, and pixel-based loss designs to enhance segmentation accuracy and robustness. SharpMin improves generalization by finding flat minima in the loss landscape, thereby reducing overfitting. Our method was evaluated in the CVPR24 MedSAM on Laptop challenge, where U-MedSAM demonstrated promising performance.

Challenge Summary U-MedSAM: Uncertainty-aware MedSAM for Medical Image Segmentation

TL;DR

The paper tackles uncertainty in medical image segmentation when using foundation models like MedSAM. It introduces U-MedSAM, which integrates an uncertainty-aware loss that combines pixel, region, and distribution components, and trains with a Sharpness-Aware Minimization optimizer to enhance generalization. A knowledge-distillation step from TinyViT to EfficientViT reduces computational load while preserving performance. On the CVPR24 MedSAM on Laptop challenge, U-MedSAM demonstrates improved Dice scores and robustness across modalities, highlighting gains in accuracy and resilience to uncertain predictions in diverse clinical images.

Abstract

Medical Image Foundation Models have proven to be powerful tools for mask prediction across various datasets. However, accurately assessing the uncertainty of their predictions remains a significant challenge. To address this, we propose a new model, U-MedSAM, which integrates the MedSAM model with an uncertainty-aware loss function and the Sharpness-Aware Minimization (SharpMin) optimizer. The uncertainty-aware loss function automatically combines region-based, distribution-based, and pixel-based loss designs to enhance segmentation accuracy and robustness. SharpMin improves generalization by finding flat minima in the loss landscape, thereby reducing overfitting. Our method was evaluated in the CVPR24 MedSAM on Laptop challenge, where U-MedSAM demonstrated promising performance.
Paper Structure (22 sections, 3 equations, 6 figures, 5 tables)

This paper contains 22 sections, 3 equations, 6 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Preprocessing
  4. Proposed Method
  5. Knowledge Distillation
  6. Uncertainty-aware Auto-learning.
  7. Sharpness-Aware Minimization.
  8. Post-processing
  9. Experiments
  10. Dataset and evaluation measures
  11. Implementation details
  12. Environment settings
  13. Training protocols
  14. Results and discussion
  15. Quantitative results on validation set
  16. ...and 7 more sections

Figures (6)

  • Figure 1: Overview of the MedSAM MedSAM.
  • Figure 2: Overview of our proposed U-MedSAM model: Building on the MedSAM architecture, we encode input images and introduce a novel uncertainty-aware learning method for automatic weight learning across multiple loss functions. The Sharpness-Aware Minimization (SharpMin) optimizer is employed, operating within a flattened loss landscape to enhance the generalization.
  • Figure 3: Qualitative results of well-segmented from various modalities.
  • Figure 4: Qualitative results of challenging examples from various modalities.
  • Figure 5: Scribble-based cases with good results for various modality segmentations.
  • ...and 1 more figures