Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features

BMC Bioinformatics

Methodology Article

Maode Lai¹  Eric I-Chao Chang²  Liang-Bo Wang³  Fang Zhang⁴  Zhipeng Jia⁴  Yuqing Ai⁴  Yan Xu⁵ 

[1] Department of Pathology, School of Medicine, Zhejiang University, Hangzhou, China;Microsoft Research, Beijing, China;Microsoft Research, Beijing, China;Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan;Microsoft Research, Beijing, China;Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China;State Key Laboratory of Software Development Environment and Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education and Research Institute of Beihang University in Shenzhen, Beijing, China;Microsoft Research, Beijing, China;

关键词: Deep convolution activation feature;    Deep learning;    Feature learning;    Segmentation;    Classification;   

DOI  :  10.1186/s12859-017-1685-x

 received in 2016-12-28, accepted in 2017-05-15,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundHistopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited.ResultsIn this paper, we propose leveraging deep convolutional neural network (CNN) activation features to perform classification, segmentation and visualization in large-scale tissue histopathology images. Our framework transfers features extracted from CNNs trained by a large natural image database, ImageNet, to histopathology images. We also explore the characteristics of CNN features by visualizing the response of individual neuron components in the last hidden layer. Some of these characteristics reveal biological insights that have been verified by pathologists. According to our experiments, the framework proposed has shown state-of-the-art performance on a brain tumor dataset from the MICCAI 2014 Brain Tumor Digital Pathology Challenge and a colon cancer histopathology image dataset.ConclusionsThe framework proposed is a simple, efficient and effective system for histopathology image automatic analysis. We successfully transfer ImageNet knowledge as deep convolutional activation features to the classification and segmentation of histopathology images with little training data. CNN features are significantly more powerful than expert-designed features.

【 授权许可】

CC BY   
© The Author(s) 2017

【 图 表 】

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