Quoc Dang Vu

TL;DR: A novel convolutional neural network is presented for simultaneous nuclear segmentation and classification that leverages the instance-rich information encoded within the vertical and horizontal distances of nuclear pixels to their centres of mass to separate clustered nuclei, resulting in an accurate segmentation.

TL;DR: The Grand Challenge on Breast Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018) as mentioned in this paper.

TL;DR: Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics as well as heavy data augmentation in the MoNuSeg 2018 challenge.

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 MoNuSAC2020 dataset as discussed by the authors contains 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types from the International Symposium on Biomedical Imaging.