MM-UNet: A Mixed MLP Architecture for Improved Ophthalmic Image Segmentation
This work addresses segmentation challenges for ocular disease diagnosis, representing an incremental improvement by adapting MLP-based methods to a specific domain.
The paper tackled the problem of ophthalmic image segmentation by introducing MM-UNet, a Mixed MLP architecture, which achieved superior performance compared to state-of-the-art deep segmentation networks on both private AS-OCT and public fundus image datasets.
Ophthalmic image segmentation serves as a critical foundation for ocular disease diagnosis. Although fully convolutional neural networks (CNNs) are commonly employed for segmentation, they are constrained by inductive biases and face challenges in establishing long-range dependencies. Transformer-based models address these limitations but introduce substantial computational overhead. Recently, a simple yet efficient Multilayer Perceptron (MLP) architecture was proposed for image classification, achieving competitive performance relative to advanced transformers. However, its effectiveness for ophthalmic image segmentation remains unexplored. In this paper, we introduce MM-UNet, an efficient Mixed MLP model tailored for ophthalmic image segmentation. Within MM-UNet, we propose a multi-scale MLP (MMLP) module that facilitates the interaction of features at various depths through a grouping strategy, enabling simultaneous capture of global and local information. We conducted extensive experiments on both a private anterior segment optical coherence tomography (AS-OCT) image dataset and a public fundus image dataset. The results demonstrated the superiority of our MM-UNet model in comparison to state-of-the-art deep segmentation networks.