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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.

As early as in 2002, the need was declared for a public repository of experimental results for gene expression profiling. Since that time, several storage hubs for gene expression profiling data have been created, to enable profile analysis and comparison. This gene expression profiling may usually be performed using either mRNA microarray hybridization ornext-generation sequencing. However, all these big data may be heterogeneous, even if they were obtained for the same type of normal or pathologically altered organs and tissues, and have been investigated using the same experimental platform. In the current work, we have proposed a new method for analyzing the homogeneity of expression data based on the Student test. Using computational experiments, we have shown the advantage of our method in terms of computational speed for large datasets, and developed an approach to interpreting the results for the Student test application. Using a new method of data analysis, we have suggested a scheme for visualization of the overall picture of gene expression and comparison of expression profiles at different diseases and/or different stages of the same disease.

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