# Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing

arXiv: https://arxiv.org/abs/2209.14635 · HTML: https://mlsanity.com/paper/2209.14635/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 75 references that are in the map.
- First posted on arXiv 29 Sep 2022.

## Builds on (most cited first, 30 of 31)

- [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)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Learning Efficient Convolutional Networks through Network Slimming](https://mlsanity.com/paper/1708.06519/) (2017)
- [Rethinking the Value of Network Pruning](https://mlsanity.com/paper/1810.05270/) (2018)
- [Dataset Condensation with Gradient Matching](https://mlsanity.com/paper/2006.05929/) (2020)
- [Do Deep Nets Really Need to be Deep?](https://mlsanity.com/paper/1312.6184/) (2013)
- [ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression](https://mlsanity.com/paper/1707.06342/) (2017)
- [Dataset Distillation by Matching Training Trajectories](https://mlsanity.com/paper/2203.11932/) (2022)
- [Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation](https://mlsanity.com/paper/1404.0736/) (2014)
- [EIE: Efficient Inference Engine on Compressed Deep Neural Network](https://mlsanity.com/paper/1602.01528/) (2016)
- [Dataset Condensation with Differentiable Siamese Augmentation](https://mlsanity.com/paper/2102.08259/) (2021)
- [Variational Dropout Sparsifies Deep Neural Networks](https://mlsanity.com/paper/1701.05369/) (2017)
- [Dataset Distillation](https://mlsanity.com/paper/1811.10959/) (2018)
- [Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning](https://mlsanity.com/paper/1602.03409/) (2016)
- [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)
- [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)
- [Low-Memory Neural Network Training: A Technical Report](https://mlsanity.com/paper/1904.10631/) (2019)
- [Soft-Label Anonymous Gastric X-ray Image Distillation](https://mlsanity.com/paper/2104.02857/) (2021)
- [Are all training examples equally valuable?](https://mlsanity.com/paper/1311.6510/) (2013)
- [Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images](https://mlsanity.com/paper/2206.03009/) (2022)
- [Self-Supervised Learning for Gastritis Detection with Gastric X-ray Images](https://mlsanity.com/paper/2104.02864/) (2021)

## Built on by (most cited first, 30 of 35)

- [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)
- [Data Distillation: A Survey](https://mlsanity.com/paper/2301.04272/) (2023)
- [Accelerating Dataset Distillation via Model Augmentation](https://mlsanity.com/paper/2212.06152/) (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)
- [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)
- [Distributional Dataset Distillation with Subtask Decomposition](https://mlsanity.com/paper/2403.00999/) (2024)
- [Decoupled Audio-Visual Dataset Distillation](https://mlsanity.com/paper/2511.17890/) (2025)
- [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)
- [Dataset Distillation in Medical Imaging: A Feasibility Study](https://mlsanity.com/paper/2407.14429/) (2024)
- [Dataset Distillation for Histopathology Image Classification](https://mlsanity.com/paper/2408.09709/) (2024)
- [LCMem: A Universal Model for Robust Image Memorization Detection](https://mlsanity.com/paper/2512.14421/) (2025)
- [Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling](https://mlsanity.com/paper/2212.09281/) (2022)
- [FD2: A Dedicated Framework for Fine-Grained Dataset Distillation](https://mlsanity.com/paper/2603.25144/) (2026)
- [Closed-Form Linear-Probe Dataset Distillation for Pre-trained Vision Models](https://mlsanity.com/paper/2605.07194/) (2026)
- [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)
- [Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching](https://mlsanity.com/paper/2305.18076/) (2023)
- [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)

## Abstract (arXiv)

Background and objective: Sharing of medical data is required to enable the cross-agency flow of healthcare information and construct high-accuracy computer-aided diagnosis systems. However, the large sizes of medical datasets, the massive amount of memory of saved deep convolutional neural network (DCNN) models, and patients' privacy protection are problems that can lead to inefficient medical data sharing. Therefore, this study proposes a novel soft-label dataset distillation method for medical data sharing. Methods: The proposed method distills valid information of medical image data and generates several compressed images with different data distributions for anonymous medical data sharing. Furthermore, our method can extract essential weights of DCNN models to reduce the memory required to save trained models for efficient medical data sharing. Results: The proposed method can compress tens of thousands of images into several soft-label images and reduce the size of a trained model to a few hundredths of its original size. The compressed images obtained after distillation have been visually anonymized; therefore, they do not contain the private information of the patients. Furthermore, we can realize high-detection performance with a small number of compressed images. Conclusions: The experimental results show that the proposed method can improve the efficiency and security of medical data sharing.