Baizhi Wang

Baizhi Wang, Rui Yan, Wenxin Ma et al.

Histomorphology is crucial in breast cancer diagnosis. However, existing whole slide image (WSI) classification methods struggle to effectively incorporate histomorphology information, limiting their ability to capture key and fine-grained pathological features. To address this limitation, we propose a novel framework that explicitly incorporates histomorphology (tumor cellularity, cellular morphology, and tissue architecture) into WSI classification. Specifically, our approach consists of three key components: (1) estimating the importance of tumor-related histomorphology information at the patch level based on medical prior knowledge; (2) generating representative cluster-level features through histomorphology-driven cluster pooling; and (3) enabling WSI-level classification through histomorphology-driven multi-instance aggregation. With the incorporation of histomorphological information, our framework strengthens the model's ability to capture key and fine-grained pathological patterns, thereby enhancing WSI classification performance. Experimental results demonstrate its effectiveness, achieving high diagnostic accuracy for molecular subtyping and cancer subtyping. The code will be made available at https://github.com/Badgewho/HMDMIL.

Rundong Wang, Wei Ba, Ying Zhou et al.

Recent methods for pathology report generation from whole-slide image (WSI) are capable of producing slide-level diagnostic descriptions but fail to ground fine-grained statements in localized visual evidence. Furthermore, they lack control over which diagnostic details to include and how to verify them. Inspired by emerging agentic analysis paradigms and the diagnostic workflow of pathologists,who selectively examine multiple fields of view, we propose QCAgent, an agentic framework for quality-controllable WSI report generation. The core innovations of this framework are as follows: (i) it incorporates a customized critique mechanism guided by a user-defined checklist specifying required diagnostic details and constraints; (ii) it re-identifies informative regions in the WSI based on the critique feedback and text-patch semantic retrieval, a process that iteratively enriches and reconciles the report. Experiments demonstrate that by making report requirements explicitly prompt-defined, constraint-aware, and verifiable through evidence-grounded refinement, QCAgent enables controllable generation of clinically meaningful and high-coverage pathology reports from WSI.