David Tellez

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: In this article, the authors compared stain color augmentation and normalization techniques and quantified their effect on CNN classification performance using a heterogeneous dataset of hematoxylin and eosin histopathology images from 4 organs and 9 pathology laboratories.

TL;DR: In this paper, a method to automatically detect mitotic tumor cells in breast cancer tissue sections based on convolutional neural networks (CNNs) was developed, which was trained in a single-center cohort and evaluated in an independent multicenter cohort from the cancer genome atlas.

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: Neural Image Compression (NIC) as discussed by the authors is a two-step method to build convolutional neural networks for gigapixel image analysis solely using weak image-level labels, avoiding the need for fine-grained manual annotations.