Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization
This addresses AMD diagnosis in ophthalmology, but it is incremental as it builds on existing multi-modal and CNN approaches.
The paper tackles automated categorization of age-related macular degeneration (AMD) using multi-modal inputs (color fundus and OCT images) by proposing a two-stream CNN with Loose Pair training, achieving viability on a real-world dataset.
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.