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

TL;DR

U-MedSAM is a novel medical image segmentation model that incorporates uncertainty estimation and sharpness-aware optimization to improve accuracy and robustness across datasets.

Contribution

It introduces an uncertainty-aware loss function and uses SharpMin optimizer to enhance medical image segmentation performance.

Findings

Demonstrated promising results in CVPR24 MedSAM on Laptop challenge.

Improved segmentation accuracy and robustness over baseline models.

Enhanced generalization through flat minima optimization.

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.