Grad-CAM based visualization of 3D CNNs in classifying fMRI

Jiajun Fu; Meili Lu; Yifan Cao; Zhaohua Guo; Zicheng Gao

doi:10.1117/12.2643867

12 October 2022 Grad-CAM based visualization of 3D CNNs in classifying fMRI

Jiajun Fu, Meili Lu, Yifan Cao, Zhaohua Guo, Zicheng Gao

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 1234212 (2022) https://doi.org/10.1117/12.2643867
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Deep learning methods have proven promising performance in decoding specific task states based on functional magnetic resonance imaging (fMRI) of the human brain, however, they lack transparency in their decision making, in the sense that it is not straightforward to visualize the features on which the decision was made. In this study, we investigated the decoding of four sensorimotor tasks based on 3D fMRI according to 3D Convolutional Neural Network (3DCNN), and then adopted Grad-CAM algorithms to provide visual explanation from deep networks so as to support the decoding decision.

Citation Download Citation

Jiajun Fu, Meili Lu, Yifan Cao, Zhaohua Guo, and Zicheng Gao "Grad-CAM based visualization of 3D CNNs in classifying fMRI", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 1234212 (12 October 2022); https://doi.org/10.1117/12.2643867

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