Transformer-based deep learning architectures have achieved notable success across various medical image analysis tasks, driven by the global modeling capabilities of the self-attention mechanism. However, Transformer-based methods exhibit significant computational complexity and a large number of parameters, rendering them challenging to apply effectively in practical medical scenarios. Compared with Transformers, large-kernel Convolutional Neural Networks (CNNs) and Multi-Layer Perceptrons (MLPs) offer more efficient inference while retaining global contextual awareness. Therefore, we rethink the role of large-kernel CNNs and MLPs in medical image analysis and leverage them to replace the heavy self-attention operation, to strike a better balance between performance and efficiency. Specifically, we propose backbone models for medical image classification and segmentation, featured by three lightweight modules: Linear Attention Feed Forward Network (FFN) for enhancing lesion features, Spatial Encoding Module for integrating multi-scale lesion information, and Smooth Depth-Wise Convolution (DwConv) FFN for efficient interaction of channel features. Composed solely of lightweight convolutional and MLP operations, our method achieves a better balance between performance and efficiency, validated by the superior performances on five datasets with varying data scales and diseases, with 98.39% on SARS-COV2-CT-Scan, 98.12% on Monkeypox Skin Lesion Dataset, 98.58% on Large COVID-19-CT scan slice, 79.45% on Synapse and 91.28% on ACDC. The low computational cost, high-performance with limited training data, and generalizability to various of medical tasks make the proposed method a promising and practical solution for medical image classification and segmentation.
Xue et al. (Mon,) studied this question.