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 ...

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 ...

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 ...

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 ...

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.

Foundation models, advanced artificial intelligence systems trained on large-scale datasets, hold the potential to provide unprecedented advancements for the medical field. In computational pathology ...

Researchers at the University of California, Los Angeles have developed a compact, cost-effective diagnostic platform ...

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 ...

Classiq and Pontificia Universidad Católica de Chile (UC Chile) have announced a joint research project to develop hybrid quantum algorithms for biomedical image analysis – assisted by classical ...

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 ...