Homma Noriyasu, Zhang Xiaoyong, Qureshi Amber, Konno Takuya, Kawasumi Yusuke, Usui Akihito, Funayama Masato, Bukovsky Ivo, Ichiji Kei, Sugita Norihiro, Yoshizawa Makoto
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1262-1265. doi: 10.1109/EMBC44109.2020.9175731.
Feasibility of computer-aided diagnosis (CAD) systems has been demonstrated in the field of medical image diagnosis. Especially, deep learning based CAD systems showed high performance thanks to its capability of image recognition. However, there is no CAD system developed for post-mortem imaging diagnosis and thus it is still unclear if the CAD system is effective for this purpose. Particulally, the drowning diagnosis is one of the most difficult tasks in the field of forensic medicine because findings of the post-mortem image diagnosis are not specific. To address this issue, we develop a CAD system consisting of a deep convolution neural network (DCNN) to classify post-mortem lung computed tomography (CT) images into two categories of drowning and non-drowning cases. The DCNN was trained by means of transfer learning and performance evaluation was conducted by 10-fold cross validation using 140 drowning cases and 140 non-drowning cases of the CT images. The area under the receiver operating characteristic curve (AUC-ROC) for the DCNN was achieved 0.88 in average. This high performance clearly demonstrated that the proposed DCNN based CAD system has a potential for post-mortem image diagnosis of drowning.
计算机辅助诊断（CAD）系统在医学图像诊断领域的可行性已得到证实。特别是，基于深度学习的CAD系统由于其图像识别能力而表现出高性能。然而，目前尚未开发出用于尸检成像诊断的CAD系统，因此该CAD系统在此目的上是否有效仍不清楚。特别是，溺水诊断是法医学领域最困难的任务之一，因为尸检图像诊断的结果并不具有特异性。为了解决这个问题，我们开发了一个由深度卷积神经网络（DCNN）组成的CAD系统，用于将尸检肺部计算机断层扫描（CT）图像分为溺水和非溺水两类。通过迁移学习对DCNN进行训练，并使用140例溺水病例和140例非溺水病例的CT图像进行10折交叉验证来进行性能评估。DCNN的受试者操作特征曲线下面积（AUC-ROC）平均达到0.88。这种高性能清楚地表明，所提出的基于DCNN的CAD系统在溺水尸检图像诊断方面具有潜力。
