Muhammad Shaban

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: Two computer algorithms are presented; one designed for segmentation of nuclei and the other for classification of whole slide tissue images, both of which were evaluated in the MICCAI 2017 Digital Pathology challenge.

TL;DR: The proposed Micro‐Net is aimed at better object localization in the face of varying intensities and is robust to noise, and compares the results on publicly available data sets and shows that the proposed network outperforms recent deep learning algorithms.

TL;DR: A novel cell-graph convolutional neural network (CGC-Net) that converts each large histology image into a graph, where each node is represented by a nucleus within the original image and cellular interactions are denoted as edges between these nodes according to node similarity.

TL;DR: The proposed TILAb score is a digital biomarker which is based on more accurate classification of tumour and lymphocytic regions, is motivated by the biological definition of TILs as tumour infiltrating lymphocytes, with the added advantages of objective and reproducible quantification.