# Dataset Distillation for Medical Dataset Sharing

arXiv: https://arxiv.org/abs/2209.14603 · HTML: https://mlsanity.com/paper/2209.14603/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 33 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Builds on (most cited first, 23 of 23)

- [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)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Dataset Condensation with Gradient Matching](https://mlsanity.com/paper/2006.05929/) (2020)
- [Dataset Distillation by Matching Training Trajectories](https://mlsanity.com/paper/2203.11932/) (2022)
- [Dynamic Few-Shot Visual Learning without Forgetting](https://mlsanity.com/paper/1804.09458/) (2018)
- [Dataset Condensation with Distribution Matching](https://mlsanity.com/paper/2110.04181/) (2021)
- [Dataset Condensation with Differentiable Siamese Augmentation](https://mlsanity.com/paper/2102.08259/) (2021)
- [Dataset Distillation](https://mlsanity.com/paper/1811.10959/) (2018)
- [Dataset Meta-Learning from Kernel Ridge-Regression](https://mlsanity.com/paper/2011.00050/) (2020)
- [Dataset Distillation with Infinitely Wide Convolutional Networks](https://mlsanity.com/paper/2107.13034/) (2021)
- [Dataset Distillation using Neural Feature Regression](https://mlsanity.com/paper/2206.00719/) (2022)
- [Privacy for Free: How does Dataset Condensation Help Privacy?](https://mlsanity.com/paper/2206.00240/) (2022)
- [Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data](https://mlsanity.com/paper/1912.07768/) (2019)
- [Flexible Dataset Distillation: Learn Labels Instead of Images](https://mlsanity.com/paper/2006.08572/) (2020)
- [Practical Coreset Constructions for Machine Learning](https://mlsanity.com/paper/1703.06476/) (2017)
- [Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning](https://mlsanity.com/paper/2004.09363/) (2020)
- [Soft-Label Anonymous Gastric X-ray Image Distillation](https://mlsanity.com/paper/2104.02857/) (2021)
- [Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments](https://mlsanity.com/paper/2208.11311/) (2022)
- [Sample Condensation in Online Continual Learning](https://mlsanity.com/paper/2206.11849/) (2022)
- [Condensed Composite Memory Continual Learning](https://mlsanity.com/paper/2102.09890/) (2021)
- [Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images](https://mlsanity.com/paper/2206.03009/) (2022)

## Built on by (most cited first, 25 of 25)

- [Dataset Distillation: A Comprehensive Review](https://mlsanity.com/paper/2301.07014/) (2023)
- [A Comprehensive Survey of Dataset Distillation](https://mlsanity.com/paper/2301.05603/) (2023)
- [Accelerating Dataset Distillation via Model Augmentation](https://mlsanity.com/paper/2212.06152/) (2022)
- [A Survey on Dataset Distillation: Approaches, Applications and Future Directions](https://mlsanity.com/paper/2305.01975/) (2023)
- [Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors](https://mlsanity.com/paper/2405.11525/) (2024)
- [Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory](https://mlsanity.com/paper/2406.19827/) (2024)
- [Importance-Aware Adaptive Dataset Distillation](https://mlsanity.com/paper/2401.15863/) (2024)
- [Generative Dataset Distillation: Balancing Global Structure and Local Details](https://mlsanity.com/paper/2404.17732/) (2024)
- [Generative Dataset Distillation Based on Diffusion Model](https://mlsanity.com/paper/2408.08610/) (2024)
- [Information-Guided Diffusion Sampling for Dataset Distillation](https://mlsanity.com/paper/2507.04619/) (2025)
- [Task-Specific Generative Dataset Distillation with Difficulty-Guided Sampling](https://mlsanity.com/paper/2507.03331/) (2025)
- [Decoupled Audio-Visual Dataset Distillation](https://mlsanity.com/paper/2511.17890/) (2025)
- [Federated Learning on Virtual Heterogeneous Data with Local-global Distillation](https://mlsanity.com/paper/2303.02278/) (2023)
- [Image Distillation for Safe Data Sharing in Histopathology](https://mlsanity.com/paper/2406.13536/) (2024)
- [Progressive trajectory matching for medical dataset distillation](https://mlsanity.com/paper/2403.13469/) (2024)
- [Generative Dataset Distillation Based on Self-knowledge Distillation](https://mlsanity.com/paper/2501.04202/) (2025)
- [Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling](https://mlsanity.com/paper/2212.09281/) (2022)
- [Boosting the Cross-Architecture Generalization of Dataset Distillation through an Empirical Study](https://mlsanity.com/paper/2312.05598/) (2023)
- [High-Order Progressive Trajectory Matching for Medical Image Dataset Distillation](https://mlsanity.com/paper/2509.24177/) (2025)
- [RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images](https://mlsanity.com/paper/2211.00313/) (2022)
- [Efficient Video Dataset Distillation via Cluster-Guided Prototype Blending](https://mlsanity.com/paper/2608.03269/) (2026)
- [Efficient and Privacy-Preserved Link Prediction via Condensed Graphs](https://mlsanity.com/paper/2503.12156/) (2025)
- [Dataset Distillation with Probabilistic Latent Features](https://mlsanity.com/paper/2505.06647/) (2025)
- [Foreground-Aware Dataset Distillation via Dynamic Patch Selection](https://mlsanity.com/paper/2601.02727/) (2026)
- [Difficulty-guided Sampling: Bridging the Target Gap between Dataset Distillation and Downstream Tasks](https://mlsanity.com/paper/2601.10090/) (2026)

## Abstract (arXiv)

Sharing medical datasets between hospitals is challenging because of the privacy-protection problem and the massive cost of transmitting and storing many high-resolution medical images. However, dataset distillation can synthesize a small dataset such that models trained on it achieve comparable performance with the original large dataset, which shows potential for solving the existing medical sharing problems. Hence, this paper proposes a novel dataset distillation-based method for medical dataset sharing. Experimental results on a COVID-19 chest X-ray image dataset show that our method can achieve high detection performance even using scarce anonymized chest X-ray images.