Architecture, size, and shape of glands are most important patterns used by pathologists for assessment of cancer malignancy in prostate histopathological tissue slides. Varying structures of glands along with cumbersome manual observations may result in subjective and inconsistent assessment. Cribriform gland with irregular border is an important feature in Gleason pattern 4. We propose using deep neural networks for cribriform pattern classification in prostate 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. Our automated image classification system analyses the H\&E images to classify them as either `Cribriform' or `Non-cribriform'. Our system uses various deep learning approaches and hand-crafted image pixel intensity-based features. We present our results for cribriform pattern detection across various parameters and configuration allowed by our system. The combination of fine-tuned deep learning models outperformed the state-of-art nuclei feature based methods. Our image classification system achieved the testing accuracy of 85.93±7.54 (cross-validated) and 88.04±5.63 ( additional unseen test set) across three folds. In this paper, we present an annotated cribriform dataset along with analysis of deep learning models and hand-crafted features for cribriform pattern detection in prostate histopathological images.
@article{arxiv.1910.04030,
title = {Cribriform pattern detection in prostate histopathological images using deep learning models},
author = {Malay Singh and Emarene Mationg Kalaw and Wang Jie and Mundher Al-Shabi and Chin Fong Wong and Danilo Medina Giron and Kian-Tai Chong and Maxine Tan and Zeng Zeng and Hwee Kuan Lee},
journal= {arXiv preprint arXiv:1910.04030},
year = {2019}
}