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 ...

The results of research by a University of Warwick-led team warn that popular deep learning systems trained for cancer pathology may be falling short on predictive accuracy because they are missing ...

Pathology laboratories are big data environments. However, these big data are often hidden behind expert humans who manually and with great care visually parse large complex and detailed datasets to ...

A team of Dana-Farber researchers has identified a potential new way to assess clinically valuable features of clear cell renal cell carcinoma (ccRCC), a form of kidney cancer, using image processing ...

Deep learning (DL) has shown great potential in digital pathology applications. The robustness of a diagnostic DL-based solution is essential for safe clinical deployment. In this work we evaluate if ...

Despite great advances, molecular cancer pathology is often limited to the use of a small number of biomarkers rather than the whole transcriptome, partly due to computational challenges. Here, we ...

Rare diseases are often difficult to diagnose and predicting the best course of treatment can be challenging for clinicians. Investigators from the Mahmood Lab at Brigham and Women's Hospital, a ...

The International Association for the Study of Lung Cancer Early Lung Imaging Confederation Tumor stage and grade, visually assessed by pathologists from evaluation of pathology images in conjunction ...

It is a renaissance for companies that sell GPU-dense systems and low-power clusters that are right for handling AI inference workloads, especially as they look to the healthcare market–one that for a ...

The International Association for the Study of Lung Cancer Early Lung Imaging Confederation Tumor stage and grade, visually assessed by pathologists from evaluation of pathology images in conjunction ...