Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …

Significant advances in artificial intelligence (AI) over the past decade potentially may lead
to dramatic effects on clinical practice. Digitized histology represents an area ripe for AI …

Abstract Numerous Deep Learning (DL) classification models have been developed for a
large spectrum of medical image analysis applications, which promises to reshape various …

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning
objective characterizations of histopathologic biomarkers in anatomic pathology. However …

Integrating large language models (LLMs) to retrieve targeted medical knowledge from
electronic health records enables significant advancements in medical research. However …

Considering the profound transformation affecting pathology practice, we aimed to develop
a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide …

In pathology, the deployment of artificial intelligence (AI) in clinical settings is constrained by
limitations in data collection and in model transparency and interpretability. Here we …

We present" HoVer-UNet," an approach to distill the knowledge of the multi-branch
HoVerNet framework for nuclei instance segmentation and classification in histopathology …

Numerous Deep Learning (DL) classification models have been developed for a large
spectrum of medical image analysis applications, which promises to reshape various facets …

Simple Summary Graph Neural Networks are emerging as a powerful tool for structured data
analysis, and predictive modeling in massive multimodal datasets. In this review, we survey …