Summary of A Lightweight Neural Architecture Search Model For Medical Image Classification, by Lunchen Xie et al.
A Lightweight Neural Architecture Search Model for Medical Image Classification
by Lunchen Xie, Eugenio Lomurno, Matteo Gambella, Danilo Ardagna, Manuel Roveri, Matteo Matteucci, Qingjiang Shi
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Neural Architecture Search (NAS) algorithm, ZO-DARTS+, is introduced for automating deep learning architecture design. This differentiable NAS method improves search efficiency through a bi-level optimization approach, generating sparse probabilities. The algorithm’s effectiveness is demonstrated on five public medical datasets, achieving the accuracy of state-of-the-art solutions while reducing search times by up to three times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models can help doctors quickly and accurately diagnose patients using medical images. However, creating these models takes a lot of time and effort. To solve this problem, scientists developed a new way to automatically design deep learning architectures called Neural Architecture Search (NAS). This paper presents a new NAS algorithm called ZO-DARTS+, which is faster and more efficient than previous methods. It uses medical images from five different datasets to show that it can find models that are just as good as the best ones, but much quicker. |
Keywords
» Artificial intelligence » Deep learning » Optimization
