# A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data

arXiv: https://arxiv.org/abs/1705.08111 · HTML: https://mlsanity.com/paper/1705.08111/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2017.

## Built on by (most cited first, 5 of 5)

- [Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits](https://mlsanity.com/paper/2106.02575/) (2021)
- [Quantum Heavy-tailed Bandits](https://mlsanity.com/paper/2301.09680/) (2023)
- [On Private and Robust Bandits](https://mlsanity.com/paper/2302.02526/) (2023)
- [Multi-Armed Bandits in Brain-Computer Interfaces](https://mlsanity.com/paper/2205.09584/) (2022)
- [Source data selection for out-of-domain generalization](https://mlsanity.com/paper/2202.02155/) (2022)

## Abstract (arXiv)

With the availability of big medical image data, the selection of an adequate training set is becoming more important to address the heterogeneity of different datasets. Simply including all the data does not only incur high processing costs but can even harm the prediction. We formulate the smart and efficient selection of a training dataset from big medical image data as a multi-armed bandit problem, solved by Thompson sampling. Our method assumes that image features are not available at the time of the selection of the samples, and therefore relies only on meta information associated with the images. Our strategy simultaneously exploits data sources with high chances of yielding useful samples and explores new data regions. For our evaluation, we focus on the application of estimating the age from a brain MRI. Our results on 7,250 subjects from 10 datasets show that our approach leads to higher accuracy while only requiring a fraction of the training data.