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EMONAS-Net: Efficient multiobjective neural architecture search using surrogate-assisted evolutionary algorithm for
Maria Baldeon Calisto1, Susana K Lai-Yuen2
1Departamento de Ingeniería Industrial, Instituto de Innovación en Productividad y Logística CATENA-USFQ, Colegio de Ciencias e Ingeniería, Universidad San Francisco de Quito, Diego de Robles s/n y Vía Interoceánica, Quito 170901, Ecuador.
Artificial Intelligence in Medicine
|September 17, 2021
Summary
This study introduces EMONAS-Net, an efficient multi-objective neural architecture search (NAS) framework for 3D medical image segmentation. It automates the design of accurate and smaller networks, reducing search time by over 50%.
Area of Science:
- Artificial Intelligence
- Medical Imaging
- Computer Vision
Background:
- Deep learning is vital for medical image segmentation but manual network design is complex and time-consuming.
- Existing multi-objective neural architecture search (NAS) methods have limitations in handling volumetric data and search efficiency.
- Over-parameterized and task-specific networks are common due to the challenges in automated design.
Purpose of the Study:
- To present EMONAS-Net, an efficient multi-objective NAS framework for 3D medical image segmentation.
- To optimize both segmentation accuracy and network size automatically.
- To address limitations of existing NAS methods in terms of search space and efficiency for volumetric data.
Main Methods:
- Developed EMONAS-Net with a novel search space considering micro- and macro-architecture structures.
- Employed a Surrogate-assisted Multiobjective Evolutionary based Algorithm (SaMEA) for efficient hyperparameter optimization.
- Integrated a Random Forest surrogate model to accelerate fitness evaluation of candidate architectures.
Main Results:
- EMONAS-Net achieved superior or comparable performance to state-of-the-art NAS methods on prostate, hippocampus, and cardiac segmentation tasks.
- The framework generated considerably smaller network architectures.
- Architecture search time was reduced by more than 50% across benchmarks.
Conclusions:
- EMONAS-Net provides an efficient and effective automated approach for designing 3D medical image segmentation networks.
- The framework successfully balances segmentation accuracy and network size.
- SaMEA algorithm and surrogate modeling significantly improve NAS efficiency for volumetric medical imaging.
