TITLE: DiagSet: a dataset for prostate cancer histopathological image classification 

ABSTRACT: Cancer diseases constitute one of the most significant societal challenges.
In this paper we introduce a novel histopathological dataset for prostate
cancer detection. The proposed dataset, consisting of over 2.6 million tissue
patches extracted from 430 fully annotated scans, 4675 scans with assigned
binary diagnosis, and 46 scans with diagnosis given independently by a group of
histopathologists, can be found at https://ai-econsilio.diag.pl. Furthermore,
we propose a machine learning framework for detection of cancerous tissue
regions and prediction of scan-level diagnosis, utilizing thresholding and
statistical analysis to abstain from the decision in uncertain cases. During
the experimental evaluation we identify several factors negatively affecting
the performance of considered models, such as presence of label noise, data
imbalance, and quantity of data, that can serve as a basis for further
research. The proposed approach, composed of ensembles of deep neural networks
operating on the histopathological scans at different scales, achieves 94.6%
accuracy in patch-level recognition, and is compared in a scan-level diagnosis
with 9 human histopathologists.
Cancer diseases constitute one of the most significant societal challenges.
In this paper we introduce a novel histopathological dataset for prostate
cancer detection. The proposed dataset, consisting of over 2.6 million tissue
patches extracted from 430 fully annotated scans, 4675 scans with assigned
binary diagnosis, and 46 scans with diagnosis given independently by a group of
histopathologists, can be found at https://ai-econsilio.diag.pl. Furthermore,
we propose a machine learning framework for detection of cancerous tissue
regions and prediction of scan-level diagnosis, utilizing thresholding and
statistical analysis to abstain from the decision in uncertain cases. During
the experimental evaluation we identify several factors negatively affecting
the performance of considered models, such as presence of label noise, data
imbalance, and quantity of data, that can serve as a basis for further
research. The proposed approach, composed of ensembles of deep neural networks
operating on the histopathological scans at different scales, achieves 94.6%
accuracy in patch-level recognition, and is compared in a scan-level diagnosis
with 9 human histopathologists.
DiagSet: a dataset for prostate cancer histopathological image classification
http://arxiv.org/abs/2105.04014v1