README.md

---
license: cc-by-4.0
pretty_name: >-
  Revisiting Automatic Data Curation for Vision Foundation Models in Digital
  Pathology
---

# Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology

**Abstract** Vision foundation models (FMs) are accelerating the devel- opment of digital pathology algorithms and transforming biomedical research. These models learn, in a self-supervised manner, to represent histological features in highly heterogeneous tiles extracted from whole-slide images (WSIs) of real-world patient samples. The performance of these FMs is significantly influenced by the size, diversity, and balance of the pre-training data. However, data selection has been primarily guided by expert knowledge at the WSI level, focusing on factors such as disease classification and tissue types, while largely overlooking the granular details available at the tile level. In this paper, we investigate the potential of unsupervised automatic data curation at the tile-level, taking into account 350 million tiles. Specifically, we apply hierarchical clustering trees to pre-extracted tile embeddings, allowing us to sample balanced datasets uniformly across the embedding space of the pretrained FM. We further identify these datasets are subject to a trade-off between size and balance, potentially compromising the quality of representations learned by FMs, and propose tailored batch sampling strategies to mitigate this effect. We demonstrate the effectiveness of our method through improved performance on a diverse range of clinically relevant downstream tasks.

## Data
We provide the following data:
1. **Clustering result:**
   - `clustering_results/clustering_{t1,t2}.csv`: Hierarchical cluster labels for each of the 350M tiles for mode t1 and t2 as csv files. The csv contains the slide_id (WSI the tile originates from), (x,y)-coordinate of the tile at level 0 (highest pyramid level of the WSI) and cluster labels for each level (denoted as columns "level_1"..."level_4"). Then slides can be downloaded from the [TCGA](https://portal.gdc.cancer.gov/) and [GTEx](https://www.gtexportal.org/home/histologyPage) websites, for tile extraction from the WSIs we recommend [openslide](https://openslide.org/api/python/).
     Structure of `clustering_results/clustering_{t1,t2}.csv`:

    	| slide\_id                                         | tile\_x | tile\_y | level\_1 | level\_2 | level\_3 | level\_4 |
    	| ------------------------------------------------- | ------- | ------- | -------- | -------- | -------- | -------- |
    	| TCGA-22-1017-01Z-00-DX1.9562FE79-A261-42D3-B39... | 32406   | 10621   | 1301309  | 17404    | 2        | 24       |
    	| TCGA-22-1017-01Z-00-DX1.9562FE79-A261-42D3-B39... | 32850   | 10621   | 3481104  | 17557    | 343      | 8        |
    	| TCGA-22-1017-01Z-00-DX1.9562FE79-A261-42D3-B39... | 30630   | 11064   | 2269415  | 34147    | 2        | 24       |
    	| TCGA-22-1017-01Z-00-DX1.9562FE79-A261-42D3-B39... | 31074   | 11064   | 3352403  | 3486     | 2        | 24       |
    	| TCGA-22-1017-01Z-00-DX1.9562FE79-A261-42D3-B39... | 31519   | 11064   | 3352388  | 11187    | 2        | 24       |

      **slide\_id**: Unique identifier for the slide image, **tile\_x, tile\_y**: Coordinates of the tile within the slide at level 0, the highest pyramid level. The tiles are of size `224px X 224px`at 20x magnification (=`112um X 112um`), **level\_1 to level\_4**: Hierarchical cluster labels the tile is associated with.
   - `clustering_results/kmeans_centroids`: K-means cluster centroids at each level. This allows associating any 224x224px tiles encoded with UNI to the nearest kmeans cluster at each level.
3. **Visualization tool**: We provide (1) UMAP coordinates for 2 million curated tiles in [metadata_N=2M.csv](https://huggingface.co/datasets/swiss-ai/patho-ssl-data-curation/blob/main/visualization_tool/metadata_N%3D2M.csv) for the t1 setting (62 level_4 clusters), and (2) tile images for a representative subset of 500k tiles, packaged in 10 tar files (`tiles_0000.tar`-`tiles_0009.tar`, each containing 224×224px pngs). (3) The file [metadata.csv](https://huggingface.co/datasets/swiss-ai/patho-ssl-data-curation/blob/main/visualization_tool/metadata.csv) contains metadata corresponding specifically to this 500k tile subset. We only provide the 500k tiles and not the full 2M due to file size limitations. Please download all files for usage with our visualization tool code at [Visualization Tool Github](https://github.com/lely475/patho-ssl-data-curation/tree/main/visualization_tool). 
## Citation
Please cite our publication if you use the provided data.
```
@misc{chen2025revisitingautomaticdatacuration,
      title={Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology}, 
      author={Boqi Chen and Cédric Vincent-Cuaz and Lydia A. Schoenpflug and Manuel Madeira and Lisa Fournier and Vaishnavi Subramanian and Sonali Andani and Samuel Ruiperez-Campillo and Julia E. Vogt and Raphaëlle Luisier and Dorina Thanou and Viktor H. Koelzer and Pascal Frossard and Gabriele Campanella and Gunnar Rätsch},
      year={2025},
      eprint={2503.18709},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.18709}, 
}
```