Model Card
[Paper] | [Github Repo] | [Cite]
What is FEATHER?
FEATHER is a collection of lightweight supervised foundation models that can easily be finetuned on consumer-grade GPUs, using orders of magnitude less parameters than slide foundation models while achieving competitive performance. It is pretrained on a challenging pan-cancer morphological classification task (PC-108, 108-way classification) on Mass General Brigham (MGB) internal dataset.
This version provides pretrained attention-based MIL (ABMIL) based on on 24K slides from MGB, with UNI patch feature encoder.
Requesting Access
As mentioned in the gated prompt, you must agree to the outlined terms of use, with the primary email for your HuggingFace account matching your institutional email. If your primary email is a personal email (@gmail/@hotmail/@qq) your request will be denied. To fix this, you can: (1) add your official institutional email to your HF account, and confirm your email address to verify, and (2) set your institutional email as your primary email in your HF account. Other reasons for your request access being denied include other mistakes in the form submitted, for example: full name includes abbreviations, affiliation is not spelled out, the described research use is not sufficient, or email domain address not recognized.
License and Terms of Use
ⓒ Mahmood Lab. This repository is released under the CC-BY-NC-ND 4.0 license and may only be used for non-commercial, academic research purposes with proper attribution. Any commercial use, sale, or other monetization of this repository is prohibited and requires prior approval. By downloading any pretrained encoder, you agree to follow the model's respective license.
Contact
For any additional questions or comments, contact Faisal Mahmood (faisalmahmood@bwh.harvard.edu),
Daniel Shao (dshao@mit.edu),
or Andrew H. Song (asong@bwh.harvard.edu)
Cite
If you find our work useful in your research, please cite our paper:
@inproceedings{shao2025do,
title={Do Multiple Instance Learning Models Transfer?},
author={Shao, Daniel and Chen, Richard J and Song, Andrew H and Runevic, Joel and Lu, Ming Y. and Ding, Tong and and Mahmood, Faisal},
booktitle={International conference on machine learning},
year={2025},
}