Title

Texture-based Deep Learning for Effective Histopathological Cancer Image Classification

Document Type

Conference Proceeding

Publication Date

11-18-2019

Publication Title

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Publisher

Institute of Electrical and Electronics Engineers

Publisher Location

San Diego, CA

Abstract

Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques, since manual examination and diagnosis with WSIs are time- and cost-consuming. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. However, despite the success of the development, there are still opportunities for further enhancements. In this paper, we propose a novel cancer texture-based deep neural network (CAT-Net) that learns scalable morphological features from histopathological WSIs. The innovation of CAT-Net is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) improving predictive performance while reducing model complexity. Moreover, CAT-Net can provide discriminative morphological (texture) patterns formed on cancerous regions of histopathological images comparing to normal regions. We elucidated how our proposed method, CAT-Net, captures morphological patterns of interest in hierarchical levels in the model. The proposed method out-performed the current state-of-the-art benchmark methods on accuracy, precision, recall, and F1 score.

Keywords

While slide images; Texture-based CNN

Disciplines

Biomedical Engineering and Bioengineering | Computer Sciences

Language

English

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