可解释人工智能：用于眼底疾病分类的普通与空洞 ResNet 模型的比较分析
Explainable AI: Comparative Analysis of Normal and Dilated ResNet Models for Fundus Disease Classification
AI总结：
本文比较普通与空洞 ResNet 在眼底疾病分类中的表现，提出以空洞卷积扩大感受野并结合可解释 AI，在 ODIR 数据集上验证空洞 ResNet 可提升分类性能并降低计算时间。
摘要：
本文提出了用于视网膜眼底图像疾病分类的空洞残差网络（ResNet）模型。在 ResNet 模型的较高层中，使用空洞卷积滤波器替代普通卷积滤波器（即空洞 ResNet），以相比普通 ResNet 模型扩大感受野，从而改善疾病分类效果。本研究引入了采用深度学习的计算机辅助诊断工具，并通过可解释人工智能技术加以增强。这些技术旨在使工具的决策过程透明化，从而使医疗专业人员能够理解并信任人工智能的诊断决策。在当前医疗健康领域，对人工智能应用透明度的需求日益增长，以确保其可靠性和符合伦理的使用，这些技术尤其具有现实意义。空洞 ResNet 被用作普通 ResNet 的替代方案，以提高视网膜眼病分类的准确率并减少所需的计算时间。本工作使用的数据集是 Ocular Disease Intelligent Recognition（ODIR）数据集，这是一个结构化的眼科数据库，包含八个类别，覆盖了大多数常见视网膜眼病。本工作使用的评估指标包括精确率、召回率、准确率和 F1 分数。在本工作中，对普通 ResNet 模型和空洞 ResNet 模型在五个变体上进行了比较研究，即 ResNet-18、ResNet-34、ResNet-50、ResNet-101 和 ResNet-152。与普通 ResNet 相比，空洞 ResNet 模型表现出有前景的结果，在上述相应变体的 ODIR 多类别疾病分类中，平均 F1 分数分别为 0.71、0.70、0.69、0.67 和 0.70。
英文摘要：
This paper presents dilated Residual Network (ResNet) models for disease classification from retinal fundus images. Dilated convolution filters are used to replace normal convolution filters in the higher layers of the ResNet model (dilated ResNet) in order to improve the receptive field compared to the normal ResNet model for disease classification. This study introduces computer-assisted diagnostic tools that employ deep learning, enhanced with explainable AI techniques. These techniques aim to make the tool's decision-making process transparent, thereby enabling medical professionals to understand and trust the AI's diagnostic decision. They are particularly relevant in today's healthcare landscape, where there is a growing demand for transparency in AI applications to ensure their reliability and ethical use. The dilated ResNet is used as a replacement for the normal ResNet to enhance the classification accuracy of retinal eye diseases and reduce the required computing time. The dataset used in this work is the Ocular Disease Intelligent Recognition (ODIR) dataset which is a structured ophthalmic database with eight classes covering most of the common retinal eye diseases. The evaluation metrics used in this work include precision, recall, accuracy, and F1 score. In this work, a comparative study has been made between normal ResNet models and dilated ResNet models on five variants namely ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152. The dilated ResNet model shows promising results as compared to normal ResNet with an average F1 score of 0.71, 0.70, 0.69, 0.67, and 0.70 respectively for the above respective variants in ODIR multiclass disease classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
