A single AI model read routine cancer tissue slides and predicted tumor type, TP53 gene changes, RNA activity and some survival-related outcomes across 32 solid cancers. Trained on more than 11,000 ...

Tissue-based diagnosis of diseases relies on the visual inspection of biopsied tissue specimens by pathologists using an optical microscope. Before putting the tissue sample under a microscope for ...

LazySlide, a new computational tool designed to connect whole-slide pathology images with RNA sequencing data through foundation models, addresses one of the persistent bottlenecks in cancer research: ...

The University of Delaware’s Comparative Pathology Laboratory focuses on diagnostic surveillance for common and emerging diseases in animal species, including commercial broiler chicken flocks on the ...

Researchers at UT Southwestern Medical Center have developed a novel artificial intelligence (AI) model that analyzes the spatial arrangement of cells in tissue samples. This innovative approach, ...

Deep learning applied to more than 25,000 histopathology slides revealed tissue-specific structural signatures of biological aging that tracked telomere attrition, pathology, comorbidity, and ...

Case Western Reserve University biomedical researchers develop first open-source, quality-control review tool for fast-growing digital pathology field There’s a low-tech problem troubling the ...

A deep-learning computer network developed through research led by Case Western Reserve University was 100 percent accurate in determining whether invasive forms of breast cancer were present in whole ...

Brown amyloid beta plaques are visible in the slide on the left, which contains a tissue sample of an untreated brain with Alzheimer's Disease. There are no plaques in the tissue sample on the right, ...