  The emerging area of computational pathology (CPath) is ripe ground for the
application of deep learning (DL) methods to healthcare due to the sheer volume
of raw pixel data in whole-slide images (WSIs) of cancerous tissue slides.
However, it is imperative for the DL algorithms relying on nuclei-level details
to be able to cope with data from `the clinical wild', which tends to be quite
challenging.
  We study, and extend recently released PanNuke dataset consisting of ~200,000
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 and just over 24,000 labeled nuclei with segmentation masks. 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.
We study the performance of segmentation and classification models when applied
to the proposed dataset and demonstrate the application of models trained on
PanNuke to whole-slide images. We provide comprehensive statistics about the
dataset and outline recommendations and research directions to address the
limitations of existing DL tools when applied to real-world CPath applications.
