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Proceedings Paper

Scale embedding shared neural networks for multiscale histological analysis of prostate cancer
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Paper Abstract

In digital pathology, deep learning approaches have been increasingly applied and shown to be effective in analyzing digitized tissue specimen images. Such approaches have, in general, chosen an arbitrary scale or resolution at which the images are analyzed for several reasons, including computational cost and complexity. However, the tissue characteristics, indicative of cancer, tend to present at differing scales. Herein, we propose a framework that enables deep convolutional neural networks to perform multiscale histological analysis of tissue specimen images in an efficient and effective manner. A deep residual neural network is shared across multiple scales, extracting high-level features. The high-level features from multiple scales are aggregated and transformed in a way that the scale information is embedded in the network. The transformed features are utilized to classify tissue images into cancer and benign. The proposed method is compared to other methodologies to combine the feature from different scales. These competing methods combine the multi-scale features via 1) concatenation 2) addition and 3) convolution. Tissue microarrays (TMAs) were employed to evaluate the proposed method and the other competing methods. Three TMAs, including 225 benign and 377 cancer tissue samples, were used as training dataset. Two TMAs with 151 benign and 252 cancer tissue samples was utilized as testing dataset. The proposed method obtained an accuracy of 0.953 and the area under the receiver operating characteristics curve (AUC) of 0.971 (95% CI: 0.955-0.987), outperforming other competing methods. This suggests that the proposed multiscale approaches via a shared neural network and scale embedding scheme, could aid in improving digital pathology analysis and cancer pathology.

Paper Details

Date Published: 18 March 2019
PDF: 6 pages
Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 1095606 (18 March 2019); doi: 10.1117/12.2512807
Show Author Affiliations
Quy Dinh Duong, Sejong Univ. (Korea, Republic of)
Dang Quoc Vu, Sejong Univ. (Korea, Republic of)
Daigeun Lee, Sejong Univ. (Korea, Republic of)
Stephen M. Hewitt, National Cancer Institute, National Institutes of Health (United States)
Kyungeun Kim, Kangbuk Samsung Hospital, Sungkyunkwan Univ. School of Medicine (Korea, Republic of)
Jin Tae Kwak, Sejong Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 10956:
Medical Imaging 2019: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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