MM-UNet: 一种用于改进眼科图像分割的混合MLP架构
MM-UNet: A Mixed MLP Architecture for Improved Ophthalmic Image Segmentation
AI总结：
针对现有眼科图像分割模型的局限，提出混合MLP架构MM-UNet，通过多尺度MLP模块实现全局与局部特征交互，在两类眼科图像数据集上均优于现有最优分割网络。
摘要：
眼科图像分割是眼部疾病诊断的关键基础。尽管全卷积神经网络（CNN）被广泛用于分割任务，但它们受归纳偏置的限制，且在建立长距离依赖关系方面存在困难。基于Transformer的模型解决了这些局限，但带来了巨大的计算开销。近期，一种简单高效的多层感知机（MLP）架构被提出用于图像分类，取得了与先进Transformer相当的性能，但其在眼科图像分割中的有效性尚未得到探索。本文提出了MM-UNet，一种专为眼科图像分割定制的高效混合MLP模型。在MM-UNet中，我们设计了多尺度MLP（MMLP）模块，通过分组策略促进不同深度的特征交互，实现全局与局部信息的同步捕获。我们在私有眼前节光学相干断层扫描（AS-OCT）图像数据集和公开眼底图像数据集上开展了大量实验，结果表明，与当前最优的深度分割网络相比，MM-UNet模型性能更优。
英文摘要：
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.
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