  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 diagnoses, and 46 scans with diagnoses independently provided by a group
of histopathologists can be found at
https://github.com/michalkoziarski/DiagSet. Furthermore, we propose a machine
learning framework for detection of cancerous tissue regions and prediction of
scan-level diagnosis, utilizing thresholding to abstain from the decision in
uncertain cases. 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 showing high statistical agreement.
