# Pseudo-domains in imaging data improve prediction of future disease status in multi-center studies

arXiv: https://arxiv.org/abs/2111.07634 · HTML: https://mlsanity.com/paper/2111.07634/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 15 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Dynamic memory to alleviate catastrophic forgetting in continuous learning settings](https://mlsanity.com/paper/2007.02639/) (2020)

## Abstract (arXiv)

In multi-center randomized clinical trials imaging data can be diverse due to acquisition technology or scanning protocols. Models predicting future outcome of patients are impaired by this data heterogeneity. Here, we propose a prediction method that can cope with a high number of different scanning sites and a low number of samples per site. We cluster sites into pseudo-domains based on visual appearance of scans, and train pseudo-domain specific models. Results show that they improve the prediction accuracy for steatosis after 48 weeks from imaging data acquired at an initial visit and 12-weeks follow-up in liver disease