搜索医学 AI 论文与资源

ICLR 2026 Poster accepted paper at ICLR 2026. Interactive segmentation uses real-time user inputs, such as mouse clicks, to iteratively refine model predictions. Although not originally designed to address distribution shifts, this paradigm naturally lends itself to such challenges. In medical imaging, where distribution shifts are common, interactive methods can use user inputs to guide models towards improved predictions. Moreover, once a model is deployed, user corrections can be used to adapt the network parameters to the new data distribution, mitigating distribution shift. Based on these insights, we aim to develop a practical, effective method for improving the adaptive capabilities of interactive segmentation models to new data distributions in medical imaging. Code/project link: https://github.com/WenTXuL/OAIMS

ICLR 2026 Poster accepted paper at ICLR 2026. Medical image segmentation is fundamental to clinical decision-making, yet existing models remain fragmented. They are usually trained on single knowledge sources and specific to individual tasks, modalities, or organs. This fragmentation contrasts sharply with clinical practice, where experts seamlessly integrate diverse knowledge: anatomical priors from training, exemplar-based reasoning from reference cases, and iterative refinement through real-time interaction. We present $\textbf{K-Prism}$, a unified segmentation framework that mirrors this clinical flexibility by systematically integrating three knowledge paradigms: (i) $\textit{semantic priors}$ learned from annotated datasets, (ii) $\textit{in-context knowledge}$ from few-shot reference examples, and (iii) $\textit{interactive feedback}$ from user inputs like clicks or scribbles. Code/project link: https://github.com/bangwayne/K-Prism

Interactive medical image segmentation benchmark and baseline from CVPR 2025, covering multiple modalities, organs, and target structures.

Grand Challenge official API lists this medical AI challenge with status OPEN_SOON. Peripelvic fractures are severe injuries with high disability and mortality rates. The PENGWIN 2026 Challenge aims to advance state-of-the-art techniques for intelligent surgical planning in 3D CT scans. It consists of three tasks: fully automated peripelvic fracture segmentation (Task 1), interactive segmentation (Task 2), and fracture reduction planning (Task 3). The dataset features 500 clinical cases with expert annotations and 16,000 simulated fracture cases to support the training of data-driven reduction models. Start date: 2026-05-10. End/deadline date: 2026-08-19.