Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization

Weisen Wang; Zhiyan Xu; Weihong Yu; Jianchun Zhao; Jingyuan Yang; Feng He; Zhikun Yang; Di Chen; Dayong Ding; Youxin Chen; Xirong Li

arXiv · 1907.12023

Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization

Weisen Wang·Zhiyan Xu·Weihong Yu·Jianchun Zhao·Jingyuan Yang·Feng He·Zhikun Yang·Di Chen·Dayong Ding·Youxin Chen·Xirong Li

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Abstract

This paper studies automated categorization of age-related macular degeneration (AMD) given a multi-modal input, which consists of a color fundus image and an optical coherence tomography (OCT) image from a specific eye. Previous work uses a traditional method, comprised of feature extraction and classifier training that cannot be optimized jointly. By contrast, we propose a two-stream convolutional neural network (CNN) that is end-to-end. The CNN's fusion layer is tailored to the need of fusing information from the fundus and OCT streams. For generating more multi-modal training instances, we introduce Loose Pair training, where a fundus image and an OCT image are paired based on class labels rather than eyes. Moreover, for a visual interpretation of how the individual modalities make contributions, we extend the class activation mapping technique to the multi-modal scenario. Experiments on a real-world dataset collected from an outpatient clinic justify the viability of our proposal for multi-modal AMD categorization.

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Weisen Wang, Zhiyan Xu, Weihong Yu, Jianchun Zhao, Jingyuan Yang, Feng He, Zhikun Yang, Di Chen, Dayong Ding, Youxin Chen, Xirong Li. 2019-07-28. Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization. https://arxiv.org/abs/1907.12023

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