Jun 1, 2020

A Deep Feature Learning Approach for Mapping the Brains Microarchitecture and Organization

BioRxiv : the Preprint Server for Biology
A. H. Balwani, Eva L. Dyer

Abstract

Models of neural architecture and organization are critical for the study of disease, aging, and development. Unfortunately, automating the process of building maps of microarchitectural differences both within and across brains still remains a challenge. In this paper, we present a way to build data-driven representations of brain structure using deep learning. With this model we can build meaningful representations of brain structure within an area, learn how different areas are related to one another anatomically, and use this model to discover new regions of interest within a sample that share similar characteristics in terms of their anatomical composition. We start by training a deep convolutional neural network to predict the brain area that it is in, using only small snapshots of its immediate surroundings. By requiring that the network learn to discriminate brain areas from these local views, it learns a rich representation of the underlying anatomical features that allow it to distinguish different brain areas. Once we have the trained network, we open up the black box, extract features from its last hidden layer, and then factorize them. After forming a low-dimensional factorization of the networks representations, w...Continue Reading

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Mentioned in this Paper

Biochemical Pathway
The Cancer Genome Atlas
Cancer Pathway
Genome
Somatic Mutation
Neoplasms
Gene Expression
Massively-Parallel Sequencing
Analysis
Malignant Neoplasms

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