搜索医学 AI 论文与资源

ICLR 2026 Poster accepted paper at ICLR 2026. Multiple Instance Learning (MIL) is the predominant framework for classifying gigapixel whole-slide images in computational pathology. MIL follows a sequence of 1) extracting patch features, 2) applying a linear layer to obtain task-specific patch features, and 3) aggregating the patches into a slide feature for classification. While substantial efforts have been devoted to optimizing patch feature extraction and aggregation, none have yet addressed the second point, the critical layer which transforms general-purpose features into task-specific features. We hypothesize that this layer constitutes an overlooked performance bottleneck and that stronger representations can be achieved with a low-rank transformation tailored to each patch's phenotype, yielding synergistic effects with any of the existing MIL approaches.

Prov-GigaPath is a pathology foundation model trained on real-world whole-slide images and released with a Nature paper and project resources.

CAMELYON17 is a digital pathology dataset for detecting breast cancer metastases in lymph node whole-slide images across multiple centers. It supports pathology classification, metastasis detection, weakly supervised learning, and domain generalization in histopathology AI.

Grand Challenge official API lists this medical AI challenge with status OPEN. BEETLE is a multicenter, multiscanner benchmark for breast cancer histopathology segmentation. It focuses on multiclass semantic segmentation of hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) into four tissue categories: invasive epithelium, non-invasive epithelium, necrosis, and other. The evaluation set comprises 170 densely annotated regions from 54 WSIs, covering all molecular subtypes and histological grades, thereby capturing much of the morphological heterogeneity seen in clinical practice. BEETLE provides a standardized resource for benchmarking breast cancer segmentation models, supporting the development of robust, generalizable algorithms for large-scale biomarke...