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Proceedings Paper

Optimized color decomposition of localized whole slide images and convolutional neural network for intermediate prostate cancer classification
Author(s): Naiyun Zhou; Yi Gao
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Paper Abstract

This paper presents a fully automatic approach to grade intermediate prostate malignancy with hematoxylin and eosin-stained whole slide images. Deep learning architectures such as convolutional neural networks have been utilized in the domain of histopathology for automated carcinoma detection and classification. However, few work show its power in discriminating intermediate Gleason patterns, due to sporadic distribution of prostate glands on stained surgical section samples. We propose optimized hematoxylin decomposition on localized images, followed by convolutional neural network to classify Gleason patterns 3+4 and 4+3 without handcrafted features or gland segmentation. Crucial glands morphology and structural relationship of nuclei are extracted twice in different color space by the multi-scale strategy to mimic pathologists’ visual examination. Our novel classification scheme evaluated on 169 whole slide images yielded a 70.41% accuracy and corresponding area under the receiver operating characteristic curve of 0.7247.

Paper Details

Date Published: 1 March 2017
PDF: 9 pages
Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400W (1 March 2017); doi: 10.1117/12.2254216
Show Author Affiliations
Naiyun Zhou, Stony Brook Univ. (United States)
Yi Gao, Stony Brook Univ. (United States)


Published in SPIE Proceedings Vol. 10140:
Medical Imaging 2017: Digital Pathology
Metin N. Gurcan; John E. Tomaszewski, Editor(s)

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