ParseCaps：一种用于医学图像诊断的可解释解析胶囊网络
ParseCaps: An Interpretable Parsing Capsule Network for Medical Image Diagnosis
AI总结：
ParseCaps通过稀疏轴向注意力路由和解析卷积胶囊层构建解析树结构，在医学图像分类中提升准确性与鲁棒性，同时提供可解释的决策依据。
摘要：
深度学习在医学图像分类中表现出色，但其临床应用因可解释性差而受到限制。胶囊网络以编码层次关系和空间特征而闻名，在解决这一问题上显示出潜力。然而，传统胶囊网络常因结构浅薄而性能不佳，而更深层的变体缺乏层次架构，从而损害了可解释性。本文提出了一种新型胶囊网络ParseCaps，利用稀疏轴向注意力路由和解析卷积胶囊层形成类似解析树的结构，增强了深度和可解释性。首先，稀疏轴向注意力路由优化了子胶囊和父胶囊之间的连接，并强调了父胶囊实例化参数上的权重分布。其次，解析卷积胶囊层生成与解析树对齐的胶囊预测。最后，基于无论概念真值是否存在都有效的损失设计，ParseCaps通过将全局胶囊的每个维度与可理解的概念相关联来推进可解释性，从而促进临床医生对模型分类结果的信任和理解。在CE-MRI、PH$^2$和Derm7pt数据集上的实验结果表明，无论概念标签是否可用，ParseCaps不仅在分类准确性、冗余减少和鲁棒性方面优于其他胶囊网络变体，而且提供了可解释的解释。
英文摘要：
Deep learning has excelled in medical image classification, but its clinical application is limited by poor interpretability. Capsule networks, known for encoding hierarchical relationships and spatial features, show potential in addressing this issue. Nevertheless, traditional capsule networks often underperform due to their shallow structures, and deeper variants lack hierarchical architectures, thereby compromising interpretability. This paper introduces a novel capsule network, ParseCaps, which utilizes the sparse axial attention routing and parse convolutional capsule layer to form a parse-tree-like structure, enhancing both depth and interpretability. Firstly, sparse axial attention routing optimizes connections between child and parent capsules, as well as emphasizes the weight distribution across instantiation parameters of parent capsules. Secondly, the parse convolutional capsule layer generates capsule predictions aligning with the parse tree. Finally, based on the loss design that is effective whether concept ground truth exists or not, ParseCaps advances interpretability by associating each dimension of the global capsule with a comprehensible concept, thereby facilitating clinician trust and understanding of the model's classification results. Experimental results on CE-MRI, PH$^2$, and Derm7pt datasets show that ParseCaps not only outperforms other capsule network variants in classification accuracy, redundancy reduction and robustness, but also provides interpretable explanations, regardless of the availability of concept labels.
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。
