Traditional task-specific computational pathology models require a substantial labeled dataset for training to perform various tasks, while foundation models can be trained on large-scale, unlabeled ...

INDIANAPOLIS—Indiana University School of Medicine Department of Pathology is launching a new Division of Computational Pathology and a Research Center for Federated Learning in Precision Medicine.

Elacestrant combinations in patients (pts) with ER+/HER2- locally advanced or metastatic breast cancer (mBC): Safety update from ELEVATE, a phase (Ph) 1b/2, open-label, umbrella study. This is an ASCO ...

In a recent study published in the journal Nature, researchers developed and evaluated the Providence Gigapixel Pathology Model (Prov-GigaPath), a whole-slide pathology foundation model, to achieve ...

Human tissue is intricate, complex and, of course, three dimensional. But the thin slices of tissue that pathologists most often use to diagnose disease are two dimensional, offering only a limited ...

Computational pathology, which assesses molecular-level features of diseases directly from tissue images (rather than testing the tissue via methods such as staining or sequencing) is making rapid ...

Pathology education and diagnostics are being reshaped by digital slides, AI models, and multimodal analysis linking tissue images to molecular data. These innovations allow faster, more accessible, ...

DeciBio Consulting LLC's latest market report, "Digital & Computational Pathology Market Report 2026-2031," states that the global digital pathology market, driven by an influx of open platforms that ...

Association of deep learning CT response assessment and interpretable components with overall survival in advanced NSCLC: Validation in a trial of sasanlimab and a real-world dataset. This is an ASCO ...