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Medical image analysis continues to hold interesting challenges given thesubtle characteristics of certain diseases and the significant overlap inappearance between diseases. In this work, we explore the concept ofself-attention for tackling such subtleties in and between diseases. To thisend, we introduce MEDUSA, a multi-scale encoder-decoder self-attentionmechanism tailored for medical image analysis. While self-attention deepconvolutional neural network architectures in existing literature center aroundthe notion of multiple isolated lightweight attention mechanisms with limitedindividual capacities being incorporated at different points in the networkarchitecture, MEDUSA takes a significant departure from this notion bypossessing a single, unified self-attention mechanism with significantly highercapacity with multiple attention heads feeding into different scales in thenetwork architecture. To the best of the authors' knowledge, this is the first"single body, multi-scale heads" realization of self-attention and enablesexplicit global context amongst selective attention at different levels ofrepresentational abstractions while still enabling differing local attentioncontext at individual levels of abstractions. With MEDUSA, we obtainstate-of-the-art performance on multiple challenging medical image analysisbenchmarks including COVIDx, RSNA RICORD, and RSNA Pneumonia Challenge whencompared to previous work. Our MEDUSA model is publicly available.

MEDUSA: Multi-scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis