Designing an unsupervised learning architecture to reconstruct brain magnetic resonance images
Year: 2021
Type: dissertation
Abstract: Brain magnetic resonance imaging (MRI) is a well-known medical imaging modality used by physicians to diagnose neurological disorders. A professional with great knowledge in the field can analyze the obtained 3D volume to detect abnormalities. The main goal of this project is to design and develop a neural network model capable of reconstructing brain MRIs by using an unsupervised approach. In essence, it has to give as result the healthy version of the brain used as input. Therefore, the model has to learn the intrinsic patterns from the healthy brains in order to transfer them to the output. For that reason, T1-weighted images from the IXI Dataset have been used to train our models only with pathology-free images. To ensure the model was learning the desired patterns, not only copying the input directly into the output, the data has been corrupted by applying different types of noise and by masking-out regions. Motivated by the autoencoders simplicity, and with the intention of generating the resulting images with better quality, some experiments have been conducted using different loss functions and trying with different types of skipped connections. The use of both residual blocks and shortcuts to skip long connections has been the combination that has led to the best results.
Author: Enric Pou Robert
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W3172523370