# Dataset Distillation in Medical Imaging: A Feasibility Study

arXiv: https://arxiv.org/abs/2407.14429 · HTML: https://mlsanity.com/paper/2407.14429/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 19 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [Dataset Condensation with Gradient Matching](https://mlsanity.com/paper/2006.05929/) (2020)
- [Dynamic Few-Shot Visual Learning without Forgetting](https://mlsanity.com/paper/1804.09458/) (2018)
- [Are we done with ImageNet?](https://mlsanity.com/paper/2006.07159/) (2020)
- [Dataset Condensation with Differentiable Siamese Augmentation](https://mlsanity.com/paper/2102.08259/) (2021)
- [DC-BENCH: Dataset Condensation Benchmark](https://mlsanity.com/paper/2207.09639/) (2022)
- [Soft-Label Anonymous Gastric X-ray Image Distillation](https://mlsanity.com/paper/2104.02857/) (2021)
- [Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing](https://mlsanity.com/paper/2209.14635/) (2022)
- [No Routing Needed Between Capsules](https://mlsanity.com/paper/2001.09136/) (2020)
- [Bridging the gap between Natural and Medical Images through Deep Colorization](https://mlsanity.com/paper/2005.10589/) (2020)
- [A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness](https://mlsanity.com/paper/2305.03355/) (2023)
- [Medical records condensation: a roadmap towards healthcare data democratisation](https://mlsanity.com/paper/2305.03711/) (2023)
- [Integrating Preprocessing Methods and Convolutional Neural Networks for Effective Tumor Detection in Medical Imaging](https://mlsanity.com/paper/2402.16221/) (2024)

## Built on by (most cited first, 4 of 4)

- [The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions](https://mlsanity.com/paper/2502.05673/) (2025)
- [Label-Consistent Dataset Distillation with Detector-Guided Refinement](https://mlsanity.com/paper/2507.13074/) (2025)
- [Improving Clinical Dataset Condensation with Mode Connectivity-based Trajectory Surrogates](https://mlsanity.com/paper/2510.05805/) (2025)
- [Distill Video Datasets into Images](https://mlsanity.com/paper/2512.14621/) (2025)

## Abstract (arXiv)

Data sharing in the medical image analysis field has potential yet remains underappreciated. The aim is often to share datasets efficiently with other sites to train models effectively. One possible solution is to avoid transferring the entire dataset while still achieving similar model performance. Recent progress in data distillation within computer science offers promising prospects for sharing medical data efficiently without significantly compromising model effectiveness. However, it remains uncertain whether these methods would be applicable to medical imaging, since medical and natural images are distinct fields. Moreover, it is intriguing to consider what level of performance could be achieved with these methods. To answer these questions, we conduct investigations on a variety of leading data distillation methods, in different contexts of medical imaging. We evaluate the feasibility of these methods with extensive experiments in two aspects: 1) Assess the impact of data distillation across multiple datasets characterized by minor or great variations. 2) Explore the indicator to predict the distillation performance. Our extensive experiments across multiple medical datasets reveal that data distillation can significantly reduce dataset size while maintaining comparable model performance to that achieved with the full dataset, suggesting that a small, representative sample of images can serve as a reliable indicator of distillation success. This study demonstrates that data distillation is a viable method for efficient and secure medical data sharing, with the potential to facilitate enhanced collaborative research and clinical applications.