Abstract
Lung cancer, a leading cause of cancer-related deaths globally, emphasises the importance of early detection for better patient outcomes. Pulmonary nodules, often early indicators of lung cancer, necessitate accurate, timely diagnosis. Despite Explainable Artificial Intelligence (XAI) advances, many existing systems struggle providing clear, comprehensive explanations, especially with limited labelled data. This study introduces MERA, a Multimodal and Multiscale self-Explanatory model designed for lung nodule diagnosis with considerably Reduced Annotation requirements. MERA integrates unsupervised and weakly supervised learning strategies (self-supervised learning techniques and Vision Transformer architecture for unsupervised feature extraction) and a hierarchical prediction mechanism leveraging sparse annotations via semi-supervised active learning in the learned latent space. MERA explains its decisions on multiple levels: model-level global explanations via semantic latent space clustering, instance-level case-based explanations showing similar instances, local visual explanations via attention maps, and concept explanations using critical nodule attributes. Evaluations on the public LIDC dataset show MERA's superior diagnostic accuracy and self-explainability. With only 1% annotated samples, MERA achieves diagnostic accuracy comparable to or exceeding state-of-the-art methods requiring full annotation. The model's inherent design delivers comprehensive, robust, multilevel explanations aligned closely with clinical practice, enhancing trustworthiness and transparency. Demonstrated viability of unsupervised and weakly supervised learning lowers the barrier to deploying diagnostic AI in broader medical domains. Our complete code is open-source available: this https URL.
Abstract (translated)
肺癌是全球癌症死亡的主要原因之一，强调了早期检测对于改善患者预后的关键性。肺结节常常是早期肺癌的标志物，需要准确且及时的确诊。尽管在可解释的人工智能（XAI）方面已经取得了一些进展，但许多现有的系统在提供清晰、全面的解释时仍然存在困难，尤其是在数据标注有限的情况下更为明显。 本研究引入了MERA模型——一种多模态和多尺度自我解释性模型，专门用于肺结节诊断，并且大幅降低了对标注数据的需求。MERA结合了无监督学习策略（包括自监督学习技术和视觉变换器架构进行无监督特征提取）以及半监督主动学习机制，在已学得的潜在空间中通过稀疏注释来进行层次化预测。 MERA在多个层面上解释其决策：模型层面的整体解释通过语义潜变量聚类实现；实例层级的情况基于解释显示类似实例；局部视觉解释利用注意图进行展示；概念解释则通过关键结节属性完成。本研究对公开的LIDC数据集进行了评估，结果显示MERA在诊断准确性和自我解释性方面都表现优越。 使用仅1%标注样本的情况下，MERA达到了与现有最先进的需要全面注释的方法相当甚至更优的诊断准确性。该模型的设计提供了全面、稳健且多层次的解释，这与临床实践非常契合，提高了可信度和透明度。无监督学习和弱监督学习方法的实际可行性降低了在更广泛的医学领域部署诊断AI技术的门槛。 我们的完整代码是开源可访问的：[网址]（请将方括号中的部分替换为实际链接）。
URL
https://arxiv.org/abs/2504.19357
