Local-Global Dual Perception Based Deep Multiple Instance Learning for Retinal Disease Classification

Qi Bi, Shuang Yu, Wei Ji, Cheng Bian, Lijun Gong, Hanruo Liu, Kai Ma, Yefeng Zheng

Abstract: With the rapidly growing number of people affected by various retinal diseases, there is a strong clinical interest for fully automatic and accurate retinal disease recognition. The unique characteristics of how retinal diseases are manifested on the fundus images pose a major challenge for automatic recognition. In order to tackle the challenges, we propose a local-global dual perception (LGDP) based deep multiple instance learning (MIL) framework that integrates the instance contribution from both local scale and global scale. The major components of the proposed framework include a local pyramid perception module (LPPM) that emphasizes the key instances from the local scale, and a global perception module (GPM) that provides a spatial weight distribution from a global scale. Extensive experiments on three major retinal disease benchmarks demonstrate that the proposed framework outperforms many state-of-the-art deep MIL methods, especially for recognizing the pathological images. Last but not least, the proposed deep MIL framework can be conveniently embedded into any convolutional backbones via a plug-and-play manner and effectively boost the performance.