Computer Aided Cancer Regions Detection of Hepatocellular Carcinoma in Whole-slide Pathological Images based on Deep Learning

Songhui Diao1; Weiren Luo2; Jiaxin Hou3; Hong Yu2; Yunqiang Chen4; Jing Xiong1; Yaoqin Xie1; Wenjian Qin1

10.1109/ICMIPE47306.2019.9098213

2019

978-1-7281-4856-4

2019 International Conference on Medical Imaging Physics and Engineering (ICMIPE)

1-6

22-24 Nov. 2019

Shenzhen, China

The emergency of whole slide imaging (WSI) in digital pathology is becoming a routine clinical diagnosis for many cancers. However, manual cancer regions review in WSIs for diagnosis is labor-intensive and error-prone task due to large scale, high-resolution and complexity of tumor heterogeneity. In this paper, we propose a fully automatic cancer region recognition framework for computer-assisted diagnostics in pathology WSIs based on deep convolutional neural network. Our framework leveraged patch-based images with image-level benign/malignant annotation for neural network training to perform a classification task instead of pixel-level segmentation, which could improve computation efficiency and alleviate annotation workload. The evaluation has been conducted on 100 liver digital whole-slide images and experimental results demonstrated our method can achieve the segmentation accuracy of 0.880 and 0.872 at 15x and 20x respectively, which is feasible and fast cancer detection for diagnosis on WSIs.

Region Detection Segmentation Pathological Images Deep Learning

其他

IEEE

1.Shenzhen Institute of Advanced Technology,Chinese Academy of Science,Shenzhen,China
2.The Second Affiliated Hospital of Southern University of Science and Technology,Department of Pathology,Shenzhen,China
3.Shenzhen Institute of Advanced Technology, Chinese Academy of Science Northeastern University,Shenzhen,China
4.12Sigma Technologies

Songhui Diao,Weiren Luo,Jiaxin Hou,et al. Computer Aided Cancer Regions Detection of Hepatocellular Carcinoma in Whole-slide Pathological Images based on Deep Learning[C],2019:1-6.