Before revision:  We study, and extend recently released PanNuke dataset consisting of   ,000 nuclei categorized into 5 clinically important classes for the challenging tasks of segmenting and classifying nuclei in WSIs \mbox{%DIFAUXCMD gamper_pannuke:_2019 . Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei \mbox{%DIFAUXCMD kumar2019multi and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham2019hover . PanNuke consists of 19 different tissue types that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to ` the clinical wild ' and with minimal selection bias.
After revision:  We study, and extend recently released PanNuke dataset consisting of  ,000 nuclei categorized into 5 clinically important classes for the challenging tasks of segmenting and classifying nuclei in WSIs . Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei \mbox{%DIFAUXCMD kumar2019multi and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham2019hover . PanNuke consists of 19 different tissue types that have been semi-automatically annotated and quality controlled by clinical pathologists, leading to a dataset with statistics similar to ` the clinical wild ' and with minimal selection bias.