Abstract
The advent of artificial intelligence (AI) has a promising role in the future long-duration spaceflight missions. Traditional AI algorithms rely on training and testing data from the same domain. However, astronaut medical data is naturally limited to a small sample size and often difficult to collect, leading to extremely limited datasets. This significantly limits the ability of traditional machine learning methodologies. Transfer learning is a potential solution to overcome this dataset size limitation and can help improve training time and performance of a neural networks. We discuss the unique challenges of space medicine in producing datasets and transfer learning as an emerging technique to address these issues.
Original language | English (US) |
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Pages (from-to) | 36-38 |
Number of pages | 3 |
Journal | Life Sciences in Space Research |
Volume | 36 |
DOIs | |
State | Published - Feb 2023 |
Keywords
- Machine learning
- Small datasets
- Space medicine
- Transfer learning
- Neural Networks, Computer
- Algorithms
- Artificial Intelligence
- Aerospace Medicine
- Machine Learning
ASJC Scopus subject areas
- Astronomy and Astrophysics
- Radiation
- Agricultural and Biological Sciences (miscellaneous)
- Health, Toxicology and Mutagenesis
- Ecology