一种用于猴痘病变分类的级联空洞卷积方法
A Cascaded Dilated Convolution Approach for Mpox Lesion Classification
AI总结：
本文提出级联空洞组注意力（CAGA）框架，结合空洞卷积与组注意力机制，以EfficientViT-L1为骨干，在猴痘皮肤图像分类中实现98%准确率并减少37.5%参数量。
摘要：
猴痘病毒被世界卫生组织列为国际关注的突发公共卫生事件（PHEIC），其全球暴发带来了重大的诊断挑战，因为它在视觉上与其他皮肤病变疾病相似。传统的猴痘诊断方法依赖临床症状和实验室检测，速度慢且劳动密集。基于深度学习的皮肤病变分类方法提供了一种有前景的替代方案。然而，开发一个在效率与准确性之间取得平衡的模型，对于在不牺牲性能的情况下确保可靠和及时的诊断至关重要。本研究引入了级联空洞组注意力（CAGA）框架来应对这些挑战，该框架结合了级联空洞注意力模块和级联组注意力机制。级联空洞注意力模块利用空洞卷积并将输出级联起来以增强多尺度表示。该模块被集成到级联组注意力机制中，以减少多头自注意力中的冗余。通过将级联空洞组注意力模块与EfficientViT-L1作为骨干架构相结合，该方法取得了最先进的性能，在猴痘近距离皮肤图像（MCSI）数据集上达到了98%的准确率，同时与原始EfficientViT-L1相比，模型参数量减少了37.5%。该模型的鲁棒性通过在两个额外的基准数据集上的广泛验证得到了证明，在这些数据集上它始终优于现有方法。
英文摘要：
The global outbreak of the Mpox virus, classified as a Public Health Emergency of International Concern (PHEIC) by the World Health Organization, presents significant diagnostic challenges due to its visual similarity to other skin lesion diseases. Traditional diagnostic methods for Mpox, which rely on clinical symptoms and laboratory tests, are slow and labor intensive. Deep learning-based approaches for skin lesion classification offer a promising alternative. However, developing a model that balances efficiency with accuracy is crucial to ensure reliable and timely diagnosis without compromising performance. This study introduces the Cascaded Atrous Group Attention (CAGA) framework to address these challenges, combining the Cascaded Atrous Attention module and the Cascaded Group Attention mechanism. The Cascaded Atrous Attention module utilizes dilated convolutions and cascades the outputs to enhance multi-scale representation. This is integrated into the Cascaded Group Attention mechanism, which reduces redundancy in Multi-Head Self-Attention. By integrating the Cascaded Atrous Group Attention module with EfficientViT-L1 as the backbone architecture, this approach achieves state-of-the-art performance, reaching an accuracy of 98% on the Mpox Close Skin Image (MCSI) dataset while reducing model parameters by 37.5% compared to the original EfficientViT-L1. The model's robustness is demonstrated through extensive validation on two additional benchmark datasets, where it consistently outperforms existing approaches.
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