Classification and visualization of chemo-brain in breast cancer survivors with deep residual and densely connected networks
Kai-Yi Lin1, Vincent Chin-Hung Chen2,3, Yuan-Hsiung Tsai2,4, and Jun-Cheng Weng1,3,5
1Department of Medical Imaging and Radiological Sciences, Graduate Institute of Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan, 2School of Medicine, Chang Gung University, Taoyuan, Taiwan, 3Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan, 4Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi, Taiwan, 5Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
Our goal was to establish objective 3D deep learning models that differentiate cerebral alterations based on the effect of chemotherapy and to visualize the pattern that was recognized by our model. The average performance of SE-ResNet-50 models was accuracy of 80%, precision of 78%, and 70% recall, and the SE-DenseNet-121 model reached identical results with an average 80% accuracy, 86% precision, and 80% recall. The regions with the greatest contributions highlighted by the integrated gradients algorithm for differentiating chemo-brain were default mode and dorsal attention networks. We hope these results will be helpful in clinically tracking chemo-brain in the future.
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