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Proceedings Paper

Multi-class breast tumor region detection for gigapixel pathology images using deep neural network with rescale approach
Author(s): Xianfei Zheng; Lingling Sun; Yaqi Wang; Longqian Ding; Yang Duan
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Paper Abstract

Breast cancer has become a worldwide disease in recent years. However, despite its growing prominence, the number of pathologists equipped to handle these cases is insufficient. Computer-aided diagnosis (CAD) system contributes to reduce costs and improve efficiency of this process. A framework based on convolutional neural networks (CNNs) which could be used to automatically detect the multi-class cancer areas on gigapixel pathology slide images was proposed. Moreover, combining the slide image characters, rescale and careful data augmentation methods were used to train the patch-based model with a small dataset. To validate the developed framework, we conducted experiments with Breast Cancer Histology Challenge (BACH) dataset and obtained International Conference on Image Analysis and Recognition (ICIAR) score of 0.582, outperforming the second-place finisher in BACH2018, for the 4-class tissue segmentation task.

Paper Details

Date Published: 6 May 2019
PDF: 7 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110692S (6 May 2019); doi: 10.1117/12.2524176
Show Author Affiliations
Xianfei Zheng, Hangzhou Dianzi Univ. (China)
Lingling Sun, Hangzhou Dianzi Univ. (China)
Yaqi Wang, Hangzhou Dianzi Univ. (China)
Longqian Ding, Hangzhou Dianzi Univ. (China)
Yang Duan, Hangzhou Dianzi Univ. (China)

Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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