Mavliutov, Yaroslav (2019) Characterization of cortical folding patterns by machine learning on graphs PRE - Research Project, ENSTA.
Deep learning (DL) has revolutionized many tasks in recent years, ranging from video processing to machine translation to audio recognition. Data of these problems are usually presented in Euclidean space. This work describes alternative DL models that are adapted to data are generated from non- Euclidean domains. These data are represented as graphs that are derived from topography, where the deepest parts of a cortical sulci are nodes of the graph. In this project, we implemented GraphSage, DCNN, GAT to solve the problem of binary node classification. In a number of experiments on real dataset, we demonstrate that selected methods outperform proposed trivial model. All models have been implemented in Python.
|Item Type:||Thesis (PRE - Research Project)|
|Subjects:||Information and Communication Sciences and Technologies|
|Deposited By:||Yaroslav MAVLIUTOV|
|Deposited On:||10 sept. 2019 11:49|
|Dernière modification:||10 sept. 2019 11:49|
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