# Confounder-Aware Medical Data Selection for Fine-Tuning Pretrained Vision Models

arXiv: https://arxiv.org/abs/2503.00744 · HTML: https://mlsanity.com/paper/2503.00744/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 2 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018)
- [Deep Learning on a Data Diet: Finding Important Examples Early in Training](https://mlsanity.com/paper/2107.07075/) (2021)
- [BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs](https://mlsanity.com/paper/2303.00915/) (2023)
- [Rotation Equivariant CNNs for Digital Pathology](https://mlsanity.com/paper/1806.03962/) (2018)
- [Data-Centric Diet: Effective Multi-center Dataset Pruning for Medical Image Segmentation](https://mlsanity.com/paper/2308.01189/) (2023)

## Built on by (most cited first, 1 of 1)

- [One-Shot Data Selection for Medical Image Classification via Graph Coverage](https://mlsanity.com/paper/2606.22002/) (2026)

## Abstract (arXiv)

The emergence of large-scale pre-trained vision foundation models has greatly advanced the medical imaging field through the pre-training and fine-tuning paradigm. However, selecting appropriate medical data for downstream fine-tuning remains a significant challenge considering its annotation cost, privacy concerns, and the detrimental effects of confounding variables. In this work, we present a confounder-aware medical data selection approach for medical dataset curation aiming to select minimal representative data by strategically mitigating the undesirable impact of confounding variables while preserving the natural distribution of the dataset. Our approach first identifies confounding variables within data and then develops a distance-based data selection strategy for confounder-aware sampling with a constrained budget in the data size. We validate the superiority of our approach through extensive experiments across diverse medical imaging modalities, highlighting its effectiveness in addressing the substantial impact of confounding variables and enhancing the fine-tuning efficiency in the medical imaging domain, compared to other data selection approaches.