# Soft-Label Anonymous Gastric X-ray Image Distillation

arXiv: https://arxiv.org/abs/2104.02857 · HTML: https://mlsanity.com/paper/2104.02857/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Gradient-based Hyperparameter Optimization through Reversible Learning](https://mlsanity.com/paper/1502.03492/) (2015)
- [Dataset Distillation](https://mlsanity.com/paper/1811.10959/) (2018)
- [Soft-Label Dataset Distillation and Text Dataset Distillation](https://mlsanity.com/paper/1910.02551/) (2019)
- [Practical Coreset Constructions for Machine Learning](https://mlsanity.com/paper/1703.06476/) (2017)
- [Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent](https://mlsanity.com/paper/1802.01737/) (2018)

## Built on by (most cited first, 30 of 51)

- [Dataset Distillation by Matching Training Trajectories](https://mlsanity.com/paper/2203.11932/) (2022)
- [Dataset Distillation using Neural Feature Regression](https://mlsanity.com/paper/2206.00719/) (2022)
- [Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation](https://mlsanity.com/paper/2211.11004/) (2022)
- [Dataset Distillation: A Comprehensive Review](https://mlsanity.com/paper/2301.07014/) (2023)
- [Improved Distribution Matching for Dataset Condensation](https://mlsanity.com/paper/2307.09742/) (2023)
- [Privacy for Free: How does Dataset Condensation Help Privacy?](https://mlsanity.com/paper/2206.00240/) (2022)
- [A Comprehensive Survey of Dataset Distillation](https://mlsanity.com/paper/2301.05603/) (2023)
- [Data Distillation: A Survey](https://mlsanity.com/paper/2301.04272/) (2023)
- [Accelerating Dataset Distillation via Model Augmentation](https://mlsanity.com/paper/2212.06152/) (2022)
- [Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments](https://mlsanity.com/paper/2208.11311/) (2022)
- [Sequential Subset Matching for Dataset Distillation](https://mlsanity.com/paper/2311.01570/) (2023)
- [Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing](https://mlsanity.com/paper/2209.14635/) (2022)
- [Dataset Distillation Using Parameter Pruning](https://mlsanity.com/paper/2209.14609/) (2022)
- [Dataset Distillation for Medical Dataset Sharing](https://mlsanity.com/paper/2209.14603/) (2022)
- [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)
- [Hyperbolic Dataset Distillation](https://mlsanity.com/paper/2505.24623/) (2025)
- [Generative Dataset Distillation Based on Diffusion Model](https://mlsanity.com/paper/2408.08610/) (2024)
- [Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory](https://mlsanity.com/paper/2505.19469/) (2025)
- [Information-Guided Diffusion Sampling for Dataset Distillation](https://mlsanity.com/paper/2507.04619/) (2025)
- [Towards Robust Dataset Learning](https://mlsanity.com/paper/2211.10752/) (2022)
- [Task-Specific Generative Dataset Distillation with Difficulty-Guided Sampling](https://mlsanity.com/paper/2507.03331/) (2025)
- [COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images](https://mlsanity.com/paper/2212.09276/) (2022)
- [MGD3: Mode-Guided Dataset Distillation using Diffusion Models](https://mlsanity.com/paper/2505.18963/) (2025)
- [Decoupled Audio-Visual Dataset Distillation](https://mlsanity.com/paper/2511.17890/) (2025)
- [Progressive trajectory matching for medical dataset distillation](https://mlsanity.com/paper/2403.13469/) (2024)
- [Label-Augmented Dataset Distillation](https://mlsanity.com/paper/2409.16239/) (2024)
- [Generative Dataset Distillation Based on Self-knowledge Distillation](https://mlsanity.com/paper/2501.04202/) (2025)
- [Dataset Distillation in Medical Imaging: A Feasibility Study](https://mlsanity.com/paper/2407.14429/) (2024)

## Abstract (arXiv)

This paper presents a soft-label anonymous gastric X-ray image distillation method based on a gradient descent approach. The sharing of medical data is demanded to construct high-accuracy computer-aided diagnosis (CAD) systems. However, the large size of the medical dataset and privacy protection are remaining problems in medical data sharing, which hindered the research of CAD systems. The idea of our distillation method is to extract the valid information of the medical dataset and generate a tiny distilled dataset that has a different data distribution. Different from model distillation, our method aims to find the optimal distilled images, distilled labels and the optimized learning rate. Experimental results show that the proposed method can not only effectively compress the medical dataset but also anonymize medical images to protect the patient's private information. The proposed approach can improve the efficiency and security of medical data sharing.