Deep learning shows high potential for many medical image analysis tasks. Neural networks  work with full-size data without extensive preprocessing and feature generation and, thus, information loss. Recent work has shown that morphological difference between specific brain regions can be found on MRI with deep learning techniques. We consider the pattern recognition task based on a large open-access dataset of healthy subjects - an exploration of brain differences between men and women . However, interpretation of the lately proposed models is based on a region of interest and can not be extended to pixel or voxel-wise image interpretation , which is considered to be more informative . In this paper, we confirm the previous findings in sex differences from diffusion-tensor imaging on T1 weighted brain MRI scans. We compare the results of three voxel-based 3D CNN interpretation methods: Meaningful Perturbations, GradCam and Guided Backpropagation and provide the open-source code .
Deep learning shows high potential for many medical image analysis tasks. Neural networks can work with full-size data without extensive preprocessing and feature generation and, thus, information loss. Recent work has shown that the morphological difference in specific brain regions can be found on MRI with the means of Convolution Neural Networks (CNN) . However, interpretation of the existing models is based on a region of interest and can not be extended to voxel-wise image interpretation on a whole image. In the current work, we consider the classification task on a large-scale open-source dataset of young healthy subjects -- an exploration of brain differences between men and women . In this paper, we extend the previous findings in gender differences from diffusion-tensor imaging on T1 brain MRI scans. We provide the voxel-wise 3D CNN interpretation comparing the results of three interpretation methods: Meaningful Perturbations, Grad CAM and Guided Backpropagation , and contribute with the open-source library .
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pixel or
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