TY - GEN
T1 - Deep Learning-Based Interpretable Computer-Aided Diagnosis of Drowning for Forensic Radiology
AU - Zeng, Yuwen
AU - Zhang, Xiaoyong
AU - Kawasumi, Yusuke
AU - Usui, Akihito
AU - Ichiji, Kei
AU - Funayama, Masato
AU - Homma, Noriyasu
N1 - Funding Information:
† This work was partially supported by Autopsy imaging Center, Tohoku University Graduate School of Medicine, and JSPS KAK-ENHI Grant Numbers JP18K19892, JP20K08012, and JP19H04479. Yuwen Zeng is the presenter of this paper.
Publisher Copyright:
© 2021 The Society of Instrument and Control Engineers-SICE.
PY - 2021/9/8
Y1 - 2021/9/8
N2 - The importance of post-mortem computed tomography (PMCT) is growing as experience and expertise in autopsy develops, with clear evidence to support the use of PMCT in suspected cases of drowning. Our previous work had proposed a computer-aided diagnosis (CAD) system to classify post-mortem lung computed tomography (CT) images into two categories of drowning and non-drowning cases. In this study, we further improved the CAD system in terms of performance and interpretability based on VGG16 deep convolutional neural network (DCNN) via transfer learning. Meanwhile, to provide forensic specialists with interpretable results, visualization was applied to post-mortem lung CT images by means of Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight important regions in the image for predicting the concept. The training and test dataset consist of 293 cases of post-mortem lung CT including 8127 slices. The area under the receiver operating characteristic curve (AUC) reached 0.983 after applying majority vote to slice-based method and the visualization showed that the proposed CAD system was interpretable and feasible for PMCT diagnosis of drowning.
AB - The importance of post-mortem computed tomography (PMCT) is growing as experience and expertise in autopsy develops, with clear evidence to support the use of PMCT in suspected cases of drowning. Our previous work had proposed a computer-aided diagnosis (CAD) system to classify post-mortem lung computed tomography (CT) images into two categories of drowning and non-drowning cases. In this study, we further improved the CAD system in terms of performance and interpretability based on VGG16 deep convolutional neural network (DCNN) via transfer learning. Meanwhile, to provide forensic specialists with interpretable results, visualization was applied to post-mortem lung CT images by means of Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight important regions in the image for predicting the concept. The training and test dataset consist of 293 cases of post-mortem lung CT including 8127 slices. The area under the receiver operating characteristic curve (AUC) reached 0.983 after applying majority vote to slice-based method and the visualization showed that the proposed CAD system was interpretable and feasible for PMCT diagnosis of drowning.
KW - Deep learning
KW - Drowning
KW - Forensic radiography
KW - Interpretability
KW - Post-mortem
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M3 - Conference contribution
AN - SCOPUS:85117693313
T3 - 2021 60th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2021
SP - 820
EP - 824
BT - 2021 60th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 60th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2021
Y2 - 8 September 2021 through 10 September 2021
ER -