结合KAN集成Transformer与膨胀邻域注意力的医学图像分类
Medical Image Classification with KAN-Integrated Transformers and Dilated Neighborhood Attention
AI总结：
本文提出MedViTV2架构，首次将KAN层引入transformer，结合增强的DiNA和分层混合策略，在29项实验中的27项达到SOTA，显著提升了医学图像分类的准确率和计算效率。
摘要：
卷积网络、transformer、混合模型以及基于Mamba的架构在各种医学图像分类任务中展现出了强大的性能。然而，这些方法主要被设计用于使用标注数据对干净图像进行分类。相比之下，现实世界的临床数据通常涉及多中心研究特有的图像损坏，这些损坏源于不同制造商成像设备的差异。在本文中，我们提出了Medical Vision Transformer (MedViTV2)，这是一种首次将Kolmogorov-Arnold Network (KAN)层引入transformer架构的新颖架构，旨在实现泛化的医学图像分类。我们开发了一个高效的KAN块，在降低计算负荷的同时提升了原始MedViT的准确率。此外，为了抵消MedViTV2在扩大规模时的脆弱性，我们提出了增强的Dilated Neighborhood Attention (DiNA)，这是对高效融合点积注意力核的改进，能够捕获全局上下文并扩大感受野，从而有效地扩展模型并解决特征崩溃问题。此外，引入了分层混合策略，以高效的方式堆叠我们的Local Feature Perception和Global Feature Perception块，这平衡了局部和全局特征感知以提升性能。在17个医学图像分类数据集和12个损坏医学图像数据集上的大量实验表明，MedViTV2在29项实验中的27项取得了最先进的结果，且降低了计算复杂度。MedViTV2的计算效率比前一版本高出44%，并显著提升了准确率，在MedMNIST上提升了4.6%，在NonMNIST上提升了5.8%，在MedMNIST-C基准上提升了13.4%。
英文摘要：
Convolutional networks, transformers, hybrid models, and Mamba-based architectures have demonstrated strong performance across various medical image classification tasks. However, these methods were primarily designed to classify clean images using labeled data. In contrast, real-world clinical data often involve image corruptions that are unique to multi-center studies and stem from variations in imaging equipment across manufacturers. In this paper, we introduce the Medical Vision Transformer (MedViTV2), a novel architecture incorporating Kolmogorov-Arnold Network (KAN) layers into the transformer architecture for the first time, aiming for generalized medical image classification. We have developed an efficient KAN block to reduce computational load while enhancing the accuracy of the original MedViT. Additionally, to counteract the fragility of our MedViT when scaled up, we propose an enhanced Dilated Neighborhood Attention (DiNA), an adaptation of the efficient fused dot-product attention kernel capable of capturing global context and expanding receptive fields to scale the model effectively and addressing feature collapse issues. Moreover, a hierarchical hybrid strategy is introduced to stack our Local Feature Perception and Global Feature Perception blocks in an efficient manner, which balances local and global feature perceptions to boost performance. Extensive experiments on 17 medical image classification datasets and 12 corrupted medical image datasets demonstrate that MedViTV2 achieved state-of-the-art results in 27 out of 29 experiments with reduced computational complexity. MedViTV2 is 44\% more computationally efficient than the previous version and significantly enhances accuracy, achieving improvements of 4.6\% on MedMNIST, 5.8\% on NonMNIST, and 13.4\% on the MedMNIST-C benchmark.
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