论文标题
多模式容量概念激活，以解释PSMA-PET/CT上转移性前列腺癌的检测和分类
Multi-modal volumetric concept activation to explain detection and classification of metastatic prostate cancer on PSMA-PET/CT
论文作者
论文摘要
可解释的人工智能（XAI）越来越多地用于分析神经网络的行为。概念激活使用人解剖概念来解释神经网络行为。这项研究旨在评估回归概念激活的可行性，以解释多模式体积数据的检测和分类。 概念验证证明是在前列腺发射断层扫描/计算机断层扫描（PET/CT）成像的转移性前列腺癌患者中证明的。多模式的体积概念激活用于提供全球和局部解释。 敏感性为80％，为每位患者1.78假阳性。全球解释表明，检测集中在CT上的解剖位置和PET对检测的信心。当地的解释表明有望有助于区分真正的积极因素和假阳性。因此，这项研究证明了使用回归概念激活来解释多模式体积数据的检测和分类的可行性。
Explainable artificial intelligence (XAI) is increasingly used to analyze the behavior of neural networks. Concept activation uses human-interpretable concepts to explain neural network behavior. This study aimed at assessing the feasibility of regression concept activation to explain detection and classification of multi-modal volumetric data. Proof-of-concept was demonstrated in metastatic prostate cancer patients imaged with positron emission tomography/computed tomography (PET/CT). Multi-modal volumetric concept activation was used to provide global and local explanations. Sensitivity was 80% at 1.78 false positive per patient. Global explanations showed that detection focused on CT for anatomical location and on PET for its confidence in the detection. Local explanations showed promise to aid in distinguishing true positives from false positives. Hence, this study demonstrated feasibility to explain detection and classification of multi-modal volumetric data using regression concept activation.
