TY - JOUR
T1 - What does deep learning see? Insights from a classifier trained to predict contrast enhancement phase from CT images
AU - Philbrick, Kenneth A.
AU - Yoshida, Kotaro
AU - Inoue, Dai
AU - Akkus, Zeynettin
AU - Kline, Timothy L.
AU - Weston, Alexander D.
AU - Korfiatis, Panagiotis
AU - Takahashi, Naoki
AU - Erickson, Bradley J.
N1 - Publisher Copyright:
© American Roentgen Ray Society.
PY - 2018/12
Y1 - 2018/12
N2 - OBJECTIVE. Deep learning has shown great promise for improving medical image classification tasks. However, knowing what aspects of an image the deep learning system uses or, in a manner of speaking, sees to make its prediction is difficult. MATERIALS AND METHODS. Within a radiologic imaging context, we investigated the utility of methods designed to identify features within images on which deep learning activates. In this study, we developed a classifier to identify contrast enhancement phase from whole-slice CT data. We then used this classifier as an easily interpretable system to explore the utility of class activation map (CAMs), gradient-weighted class activation maps (Grad-CAMs), saliency maps, guided backpropagation maps, and the saliency activation map, a novel map reported here, to identify image features the model used when performing prediction. RESULTS. All techniques identified voxels within imaging that the classifier used. SAMs had greater specificity than did guided backpropagation maps, CAMs, and Grad-CAMs at identifying voxels within imaging that the model used to perform prediction. At shallow network layers, SAMs had greater specificity than Grad-CAMs at identifying input voxels that the layers within the model used to perform prediction. CONCLUSION. As a whole, voxel-level visualizations and visualizations of the imaging features that activate shallow network layers are powerful techniques to identify features that deep learning models use when performing prediction.
AB - OBJECTIVE. Deep learning has shown great promise for improving medical image classification tasks. However, knowing what aspects of an image the deep learning system uses or, in a manner of speaking, sees to make its prediction is difficult. MATERIALS AND METHODS. Within a radiologic imaging context, we investigated the utility of methods designed to identify features within images on which deep learning activates. In this study, we developed a classifier to identify contrast enhancement phase from whole-slice CT data. We then used this classifier as an easily interpretable system to explore the utility of class activation map (CAMs), gradient-weighted class activation maps (Grad-CAMs), saliency maps, guided backpropagation maps, and the saliency activation map, a novel map reported here, to identify image features the model used when performing prediction. RESULTS. All techniques identified voxels within imaging that the classifier used. SAMs had greater specificity than did guided backpropagation maps, CAMs, and Grad-CAMs at identifying voxels within imaging that the model used to perform prediction. At shallow network layers, SAMs had greater specificity than Grad-CAMs at identifying input voxels that the layers within the model used to perform prediction. CONCLUSION. As a whole, voxel-level visualizations and visualizations of the imaging features that activate shallow network layers are powerful techniques to identify features that deep learning models use when performing prediction.
KW - CT
KW - Class activation map (CAM)
KW - Computer-aided diagnosis
KW - Contrast enhancement phase
KW - Convolutional neural network (CNN)
KW - Deep learning
KW - Gradient-weighted class activation map (Grad-CAM)
KW - Guided backpropagation
KW - Machine learning
KW - Saliency activation map
KW - Saliency map
UR - http://www.scopus.com/inward/record.url?scp=85056921390&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056921390&partnerID=8YFLogxK
U2 - 10.2214/AJR.18.20331
DO - 10.2214/AJR.18.20331
M3 - Article
C2 - 30403527
AN - SCOPUS:85056921390
SN - 0361-803X
VL - 211
SP - 1184
EP - 1193
JO - American Journal of Roentgenology
JF - American Journal of Roentgenology
IS - 6
ER -