MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model Publication date: 8 Oct 2024 Topic: Semantic Segmentation Paper: https://arxiv.org/pdf/2410.05905v1.pdf GitHub: https://github.com/yeerwen/uniseg Description: We evaluate MedUniSeg on a comprehensive multi-modal upstream dataset consisting of 17 sub-datasets. The results demonstrate that MedUniSeg achieves superior multi-task segmentation performance, attaining a 1.2% improvement in the mean Dice score across the 17 upstream tasks compared to nnUNet baselines, while using less than 1/10 of the parameters. For tasks that underperform during the initial multi-task joint training, we freeze MedUniSeg and introduce new modules to re-learn these tasks. This approach yields an enhanced version, MedUniSeg*, which consistently outperforms MedUniSeg across all tasks.