Matthew A. Suriawinata

TL;DR: An automatic image analysis method that can accurately classify different types of colorectal polyps on whole-slide images to help pathologists with this characterization and diagnosis and can reduce the cognitive burden on pathologists and improve their efficacy in histopathological characterization and follow-up recommendations.

TL;DR: This work proposes a deep-learning-based image analysis approach that not only can accurately classify different types of polyps in whole-slide images, but also generates major regions and features on the slide through a model visualization approach.

TL;DR: Wang et al. as discussed by the authors built an automatic image-understanding method that can accurately classify different types of colorectal polyps in whole-slide histology images to help pathologists with histopathological characterization and diagnosis of polyps.

TL;DR: A deep neural network model that can accurately classify digitized surgical resection slides and biopsy slides into five related classes has the potential to assist pathologists by automatically pre-screening slides to reduce false-negative cases, highlighting regions of importance on digitized slides to accelerate diagnosis, and providing objective and accurate diagnosis as the second opinion.

TL;DR: In this article, a deep neural network model was developed to classify digitized surgical resection slides and biopsy slides into five related classes: clear cell RCC, papillary RCC and chromophobe RCC.