Talha Qaiser

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 achieved results are promising given the difficulty of the tasks and weakly‐labeled nature of the ground truth, however, further research is needed to improve the practical utility of image analysis methods for this task.

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: In this paper, the authors proposed a tumor segmentation framework based on the novel concept of persistent homology profiles (PHPs), which can distinguish tumor regions from their normal counterparts by modeling the atypical characteristics of tumor nuclei.

TL;DR: This is the first study using DRL for IHC scoring and could potentially lead to wider use of DRL in the domain of computational pathology reducing the computational burden of the analysis of large multi-gigapixel histology images.