Vassili Kovalev

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: The ability of the suggested 3-D texture descriptors is demonstrated on nontrivial classification tasks for pathologic findings in brain datasets, and their sensitivity and dependence on spatial image scaling are evaluated.

TL;DR: The texture-based method reported here is based on extended multisort cooccurrence matrices that employ intensity, gradient, and anisotropy features in a uniform way and should be considered as another tool for digital morphometry in neuroscience.

TL;DR: The PANDA challenge as mentioned in this paper was organized by 1,290 developers to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies.