Nasir M. Rajpoot

TL;DR: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints.

TL;DR: The recent state of the art CAD technology for digitized histopathology is reviewed and the development and application of novel image analysis technology for a few specific histopathological related problems being pursued in the United States and Europe are described.

TL;DR: A Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection and a novel Neighboring Ensemble Predictor (NEP) coupled with CNN to more accurately predict the class label of detected cell nuclei are proposed.

TL;DR: A novel approach is proposed that simultaneously solves the problems of counting, density map estimation and localization of people in a given dense crowd image and significantly outperforms state-of-the-art on the new dataset, which is the most challenging dataset with the largest number of crowd annotations in the most diverse set of scenes.

TL;DR: An overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015 is provided, along with the method descriptions and evaluation results from the top performing methods.