Peter D. Caie

TL;DR: The utility of a cell-based assay and custom designed image analysis algorithms designed to monitor morphologic phenotypic response in detail across distinct cancer cell types are shown and have the potential to drive the development of a new generation of novel therapeutic classes encompassing pharmacologic compositions or polypharmacology in appropriate disease context.

TL;DR: This paper reviews work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlights the different ideas underlying these methodologies.

TL;DR: This work provides the complete ground-truth and test data sets, as well as open-source implementations of the various methods in a common software framework to facilitate the ready application and future development of image-based phenotypic profiling methods.

TL;DR: In this paper, the authors review work on the interdisciplinary attempt of training deep neural networks using whole-slide images, and highlight the different ideas underlying these methodologies and highlight their different approaches.

TL;DR: The investigation of the spatial relationship between lymphocytes and TBs within the tumor microenvironment improves accuracy of prognosis of patients with stage II colorectal cancer through an automated image analysis and machine learning workflow.