Lumen-based detection of prostate cancer via convolutional neural networks

Jin Tae Kwak; Stephen M. Hewitt

doi:10.1117/12.2253513

1 March 2017 Lumen-based detection of prostate cancer via convolutional neural networks

Jin Tae Kwak, Stephen M. Hewitt

Author Affiliations +

Proceedings Volume 10140, Medical Imaging 2017: Digital Pathology; 1014008 (2017) https://doi.org/10.1117/12.2253513
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

We present a deep learning approach for detecting prostate cancers. The approach consists of two steps. In the first step, we perform tissue segmentation that identifies lumens within digitized prostate tissue specimen images. Intensity- and texture-based image features are computed at five different scales, and a multiview boosting method is adopted to cooperatively combine the image features from differing scales and to identify lumens. In the second step, we utilize convolutional neural networks (CNN) to automatically extract high-level image features of lumens and to predict cancers. The segmented lumens are rescaled to reduce computational complexity and data augmentation by scaling, rotating, and flipping the rescaled image is applied to avoid overfitting. We evaluate the proposed method using two tissue microarrays (TMA) – TMA1 includes 162 tissue specimens (73 Benign and 89 Cancer) and TMA2 comprises 185 tissue specimens (70 Benign and 115 Cancer). In cross-validation on TMA1, the proposed method achieved an AUC of 0.95 (CI: 0.93-0.98). Trained on TMA1 and tested on TMA2, CNN obtained an AUC of 0.95 (CI: 0.92-0.98). This demonstrates that the proposed method can potentially improve prostate cancer pathology.

Citation Download Citation

Jin Tae Kwak and Stephen M. Hewitt "Lumen-based detection of prostate cancer via convolutional neural networks", Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 1014008 (1 March 2017); doi: 10.1117/12.2253513; https://doi.org/10.1117/12.2253513

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