# Image Distillation for Safe Data Sharing in Histopathology

arXiv: https://arxiv.org/abs/2406.13536 · HTML: https://mlsanity.com/paper/2406.13536/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 36 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 25 of 25)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [All are Worth Words: A ViT Backbone for Diffusion Models](https://mlsanity.com/paper/2209.12152/) (2022)
- [Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning](https://mlsanity.com/paper/2206.02647/) (2022)
- [MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis](https://mlsanity.com/paper/2010.14925/) (2020)
- [Generalizing Dataset Distillation via Deep Generative Prior](https://mlsanity.com/paper/2305.01649/) (2023)
- [Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection](https://mlsanity.com/paper/2207.01463/) (2022)
- [Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification](https://mlsanity.com/paper/2307.15254/) (2023)
- [Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing](https://mlsanity.com/paper/2209.14635/) (2022)
- [ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image Classification](https://mlsanity.com/paper/2207.01805/) (2022)
- [LifeLonger: A Benchmark for Continual Disease Classification](https://mlsanity.com/paper/2204.05737/) (2022)
- [Dataset Distillation for Medical Dataset Sharing](https://mlsanity.com/paper/2209.14603/) (2022)
- [ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis](https://mlsanity.com/paper/2308.13324/) (2023)
- [NASDM: Nuclei-Aware Semantic Histopathology Image Generation Using Diffusion Models](https://mlsanity.com/paper/2303.11477/) (2023)
- [Synthetic Augmentation with Large-scale Unconditional Pre-training](https://mlsanity.com/paper/2308.04020/) (2023)
- [Hierarchical discriminative learning improves visual representations of biomedical microscopy](https://mlsanity.com/paper/2303.01605/) (2023)
- [MixUp-MIL: Novel Data Augmentation for Multiple Instance Learning and a Study on Thyroid Cancer Diagnosis](https://mlsanity.com/paper/2211.05862/) (2022)
- [Gene-induced Multimodal Pre-training for Image-omic Classification](https://mlsanity.com/paper/2309.02702/) (2023)
- [Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification](https://mlsanity.com/paper/2303.15749/) (2023)
- [Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions](https://mlsanity.com/paper/2305.18830/) (2023)
- [Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning](https://mlsanity.com/paper/2306.00047/) (2023)
- [SLPD: Slide-level Prototypical Distillation for WSIs](https://mlsanity.com/paper/2307.10696/) (2023)
- [Centrality in Modular Networks](https://mlsanity.com/paper/1810.05101/) (2018)
- [The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks](https://mlsanity.com/paper/2310.01144/) (2023)

## Built on by (most cited first, 6 of 6)

- [Dataset Distillation with Neural Characteristic Function: A Minmax Perspective](https://mlsanity.com/paper/2502.20653/) (2025)
- [The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions](https://mlsanity.com/paper/2502.05673/) (2025)
- [Data-Efficient Generation for Dataset Distillation](https://mlsanity.com/paper/2409.03929/) (2024)
- [Dataset Distillation with Probabilistic Latent Features](https://mlsanity.com/paper/2505.06647/) (2025)
- [InfoMotion: A Graph-Based Approach to Video Dataset Distillation for Echocardiography](https://mlsanity.com/paper/2512.09422/) (2025)
- [Dataset Distillation Based on Saliency-Driven Prototype Alignment](https://mlsanity.com/paper/2607.25318/) (2026)

## Abstract (arXiv)

Histopathology can help clinicians make accurate diagnoses, determine disease prognosis, and plan appropriate treatment strategies. As deep learning techniques prove successful in the medical domain, the primary challenges become limited data availability and concerns about data sharing and privacy. Federated learning has addressed this challenge by training models locally and updating parameters on a server. However, issues, such as domain shift and bias, persist and impact overall performance. Dataset distillation presents an alternative approach to overcoming these challenges. It involves creating a small synthetic dataset that encapsulates essential information, which can be shared without constraints. At present, this paradigm is not practicable as current distillation approaches only generate non human readable representations and exhibit insufficient performance for downstream learning tasks. We train a latent diffusion model and construct a new distilled synthetic dataset with a small number of human readable synthetic images. Selection of maximally informative synthetic images is done via graph community analysis of the representation space. We compare downstream classification models trained on our synthetic distillation data to models trained on real data and reach performances suitable for practical application.