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HNAS: Hyper neural architecture search for image segmentation
Date
2021
Abstract
Deep learning is a well suited approach to successfully address image processing and there are several Neural Networks architectures proposed on this research field, one interesting example is the U-net architecture and and its variants. This work proposes to automatically find the best architecture combination from a set of the current most relevant U-net architectures by using a genetic algorithm (GA) applied to solve the Retinal Blood Vessel Segmentation (RVS), which it is relevant to diagnose and cure blindness in diabetes patients. Interestingly, the experimental results show that avoiding human-bias in the design, GA finds novel combinations of U-net architectures, which at first sight seems to be complex but it turns out to be smaller, reaching competitive performance than the manually designed architectures and reducing considerably the computational effort to evolve them.
Supervisor
Description
Publisher
ScitPress
Citation
Proceedings of the 13th International Conference on Agents and Artificial Intelligence, 2, pp. 246-256
Files
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Houreh_2021_HNAS.pdf
Adobe PDF, 7.87 MB
Funding code
Funding Information
The fourth author is partially financed by the Coordenao de Aperfeioamento de Pessoal de Nvel Superior - Brasil (CAPES) - Finance Code 001
Sustainable Development Goals
External Link
Type
Meetings and Proceedings
Rights
https://creativecommons.org/licenses/by-nc-sa/4.0/