Development and Validation of a Deep Learning-Based Microsatellite Instability Predictor from Prostate Cancer Whole-Slide Images

no code implementations • 12 Oct 2023 • Qiyuan Hu, Abbas A. Rizvi, Geoffery Schau, Kshitij Ingale, Yoni Muller, Rachel Baits, Sebastian Pretzer, Aïcha BenTaieb, Abigail Gordhamer, Roberto Nussenzveig, Adam Cole, Matthew O. Leavitt, Rohan P. Joshi, Nike Beaubier, Martin C. Stumpe, Kunal Nagpal

As such, prediction of MSI status from hematoxylin and eosin (H&E) stained whole-slide images (WSIs) could identify prostate cancer patients most likely to benefit from confirmatory testing and becoming eligible for immunotherapy.

Multiple Instance Learning whole slide images

AI-augmented histopathologic review using image analysis to optimize DNA yield and tumor purity from FFPE slides

no code implementations • 26 Mar 2022 • Bolesław L. Osinski, Aïcha BenTaieb, Irvin Ho, Ryan D. Jones, Rohan P. Joshi, Andrew Westley, Michael Carlson, Caleb Willis, Luke Schleicher, Brett M. Mahon, Martin C. Stumpe

Then, a pathologist-defined target yield divided by the predicted DNA yield/slide gives the number of slides to scrape.

Deep Learning Models for Digital Pathology

no code implementations • 27 Oct 2019 • Aïcha BenTaieb, Ghassan Hamarneh

We then discuss the challenges facing the validation and integration of such deep learning-based computational systems in clinical workflow and reflect on future opportunities for histopathology derived image measurements and better predictive modeling.