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 , generally of the order of 100K \times%DIFDELCMD < }%%% 80K pixels . 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 more than 200 \sim200 ,000 nuclei categorized into 5 clinically important classes for the challenging tasks of detecting, segmenting and classifying nuclei in WSIs %DIFDELCMD < \footnote{%%% Download dataset here URL gamper_pannuke:_2019. Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei kumar2019multi and just over 24,000 labeled nuclei with segmentation masks \mbox{%DIFAUXCMD graham_hover-net:_2019 . PanNuke consists of 19 different tissue types from over 20,000 WSIs 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.
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 %DIFDELCMD < }%%% . 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 \sim200 ,000 nuclei categorized into 5 clinically important classes for the challenging tasks of segmenting and classifying nuclei in WSIs %DIFDELCMD < \footnote{%%% gamper_pannuke:_2019. Previous pan-cancer datasets consisted of only up to 9 different tissues and up to 21,000 unlabeled nuclei 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. 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.
D
, generally of the order of 100K
D
\times
D
80K pixels
D
more than 200
D
detecting,
D
Download dataset here URL
R
\mbox{%DIFAUXCMD graham_hover-net:_2019
\mbox{%DIFAUXCMD graham2019hover
D
from over 20,000 WSIs