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 ...

When the performance of AI models was assessed within stratified patient subgroups, such as only high-grade breast cancers or only MSI-positive tumors, accuracy fell substantially, revealing that the ...

Researchers present Tripath: new, deep learning models that can use 3D pathology datasets to make clinical outcome predictions. The research team imaged curated prostate cancer specimens, using two 3D ...

Discover the impact of digital pathology on drug discovery and biomarker research this International Women’s Day with Dr ...

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 ...

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 ...

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 ...

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 ...

LOS ANGELES--(BUSINESS WIRE)--DeciBio Consulting LLC’s latest market report, “Digital & Computational Pathology Market Report 2023-2028,” states that the global digital pathology market, driven by ...