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Architecture, size, and shape of glands are most important patterns used bypathologists for assessment of cancer malignancy in prostate histopathologicaltissue slides. Varying structures of glands along with cumbersome manualobservations may result in subjective and inconsistent assessment. Cribriformgland with irregular border is an important feature in Gleason pattern 4. Wepropose using deep neural networks for cribriform pattern classification inprostate histopathological images. $163708$ Hematoxylin and Eosin (H\&E)stained images were extracted from histopathologic tissue slides of $19$patients with prostate cancer and annotated for cribriform patterns. Ourautomated image classification system analyses the H\&E images to classify themas either `Cribriform' or `Non-cribriform'. Our system uses various deeplearning approaches and hand-crafted image pixel intensity-based features. Wepresent our results for cribriform pattern detection across various parametersand configuration allowed by our system. The combination of fine-tuned deeplearning models outperformed the state-of-art nuclei feature based methods. Ourimage classification system achieved the testing accuracy of $85.93~\pm~7.54$(cross-validated) and $88.04~\pm~5.63$ ( additional unseen test set) acrossthree folds. In this paper, we present an annotated cribriform dataset alongwith analysis of deep learning models and hand-crafted features for cribriformpattern detection in prostate histopathological images.

Cribriform pattern detection in prostate histopathological images using deep learning models