MedMLP: An Efficient MLP-Like Network for Zero-Shot Retinal Image Classification

Deep neural networks (DNNs) have demonstrated superior performance compared to humans across various tasks. However, DNNs often face the challenge of domain shift, where their performance notably deteriorates when applied to medical images with distributions differing from those seen during training. To address this issue and achieve high performance in new target domains under zero-shot settings, we leverage the ability of self-attention mechanisms to capture global dependencies. We introduce a novel MLP-like model designed for superior efficiency and zero-shot robustness. Specifically, we propose an adaptive fully-connected (AdaFC) layer to overcome the fundamental limitation of traditional fully-connected layers in adapting to inputs of various sizes while maintaining GPU efficiency. Building upon AdaFC, we present a new MLP-based network architecture named MedMLP. Through our proposed training pipeline, we achieve a significant 20.1% increase in model testing accuracy on an out-of-distribution dataset, surpassing the widely used ResNet-50 model.