Name
Abstract | ||
|---|---|---|
The study of neurocognitive tasks requiring accurate localisation of activity often rely on functional Magnetic Resonance Imaging, a widely adopted technique that makes use of a pipeline of data processing modules, each involving a variety of parameters. These parameters are frequently set according to the local goal of each specific module, not accounting for the rest of the pipeline. Given recent success of neural network research in many different domains, we propose to convert the whole data pipeline into a deep neural network, where the parameters involved are jointly optimised by the network to best serve a common global goal. As a proof of concept, we develop a module able to adaptively apply the most suitable spatial smoothing to every brain volume for each specific neuroimaging task, and we validate its results in a standard brain decoding experiment. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computer Vision and Pattern Recognition | Data mining,Pipeline (computing),Data processing,Pipeline transport,End-to-end principle,Computer science,Proof of concept,Smoothing,Artificial intelligence,Decoding methods,Artificial neural network,Machine learning |
DocType | Volume | Citations |
Journal | abs/1610.04079 | 1 |
PageRank | References | Authors |
0.36 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Albert Vilamala | 1 | 5 | 2.19 |
| Kristoffer Hougaard Madsen | 2 | 145 | 18.74 |
| Lars Kai Hansen | 3 | 2776 | 341.03 |