Research & Development Microscopy plus deep learning advances prostate cancer diagnosis 12 Mar 2024 University of Washington (Seattle) develops machine-learning model to improve biopsy assessment.

Researchers assess the power of a fully automated deep learning model to identify of clinically significant prostate cancer.

A research team has created a new machine-learning framework that distinguishes between low- and high-risk prostate cancer, according to a paper published in Scientific Reports.

We propose a deep learning, histopathology image–based risk-stratification model that combines clinicopathologic data along with hematoxylin and eosin– and Ki-67–stained histopathology images.

The patches with high probability of cancer recurrence are shown by colour and height. (Courtesy: RIKEN) Researchers from the RIKEN Center for Advanced Intelligence Project (AIP) in Japan have shown ...

A new machine-learning framework enabled researchers to assess prostate cancer risk with more accuracy than the current standard methods, according to results of a single-center, retrospective ...

EDL, accurately identifies lung and colon cancers using histopathological images. This approach significantly improves early ...

A research team has created a new machine-learning framework that distinguishes between low- and high-risk prostate cancer, according to a paper published in Scientific Reports.

A recent Radiologyjournal study assesses the power of a fully automated deep learning (DL) model to produce deterministic outputs for identifying clinically significant prostate cancer (csPCa).

Prostate cancer (PCa) represents a highly heterogeneous disease that requires tools to assess oncologic risk and guide patient management and treatment planning. Current models are based on various ...