Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS).

In Journal of pathology informatics ; h5-index 23.0

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

Parwani Anil V, Patel Ankush, Zhou Ming, Cheville John C, Tizhoosh Hamid, Humphrey Peter, Reuter Victor E, True Lawrence D

2023

Artificial intelligence, Computational pathology, Digital pathology, Genitourinary pathology