Efficient Universal Models for Medical Image Segmentation via Weakly Supervised In-Context Learning
This addresses the efficiency challenge for researchers and practitioners in medical imaging by reducing annotation effort, though it is incremental as it builds on existing ICL paradigms.
The paper tackled the problem of high annotation costs in universal models for medical image segmentation by proposing Weakly Supervised In-Context Learning (WS-ICL), which uses weak prompts like bounding boxes instead of dense labels, achieving performance comparable to regular ICL models at significantly lower annotation cost.
Universal models for medical image segmentation, such as interactive and in-context learning (ICL) models, offer strong generalization but require extensive annotations. Interactive models need repeated user prompts for each image, while ICL relies on dense, pixel-level labels. To address this, we propose Weakly Supervised In-Context Learning (WS-ICL), a new ICL paradigm that leverages weak prompts (e.g., bounding boxes or points) instead of dense labels for context. This approach significantly reduces annotation effort by eliminating the need for fine-grained masks and repeated user prompting for all images. We evaluated the proposed WS-ICL model on three held-out benchmarks. Experimental results demonstrate that WS-ICL achieves performance comparable to regular ICL models at a significantly lower annotation cost. In addition, WS-ICL is highly competitive even under the interactive paradigm. These findings establish WS-ICL as a promising step toward more efficient and unified universal models for medical image segmentation. Our code and model are publicly available at https://github.com/jiesihu/Weak-ICL.