论文标题
可解释的椎骨骨折诊断
Interpretable Vertebral Fracture Diagnosis
论文作者
论文摘要
黑盒神经网络模型是否学习临床相关的骨折诊断特征？该答案不仅建立了可靠性，弥漫了科学的好奇心，而且还会导致可解释的和冗长的发现，可以帮助放射科医生在最终方面并增加信任。这项工作确定了CT图像中概念网络用于椎骨骨折诊断。这是通过将概念与与数据集中特定诊断高度相关的神经元关联而实现的。这些概念要么与放射科医生的神经元相关联，要么在特定的预测中可视化，并被视为用户的解释。我们评估哪些概念会导致正确的诊断，哪些概念导致了误报。提出的框架和分析为可靠且可解释的椎骨骨折诊断铺平了道路。
Do black-box neural network models learn clinically relevant features for fracture diagnosis? The answer not only establishes reliability quenches scientific curiosity but also leads to explainable and verbose findings that can assist the radiologists in the final and increase trust. This work identifies the concepts networks use for vertebral fracture diagnosis in CT images. This is achieved by associating concepts to neurons highly correlated with a specific diagnosis in the dataset. The concepts are either associated with neurons by radiologists pre-hoc or are visualized during a specific prediction and left for the user's interpretation. We evaluate which concepts lead to correct diagnosis and which concepts lead to false positives. The proposed frameworks and analysis pave the way for reliable and explainable vertebral fracture diagnosis.
