CellCLIP - Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning
CellCLIP, a cross-modal contrastive learning framework for HCS data. CellCLIP leverages pre-trained image encoders coupled with a novel channel encoding scheme to better capture relationships between different microscopy channels in image embeddings, along with natural language encoders for repre senting perturbations. Our framework outperforms current open-source models, demonstrating the best performance in both cross-modal retrieval and biologically meaningful downstream tasks while also achieving significant reductions in computation time.
This repository contains model checkpoints for CellCLIP trained with
Cell painting encodings: Image embeddings extracted using DINOv2-Giant and projected to a feature dimension of 1536.
Perturbation encodings: Text embeddings generated using BERT as the text encoder.
Citation
@article{lu2025cellclip,
title={CellCLIP--Learning Perturbation Effects in Cell Painting via Text-Guided Contrastive Learning},
author={Lu, Mingyu and Weinberger, Ethan and Kim, Chanwoo and Lee, Su-In},
journal={arXiv preprint arXiv:2506.06290},
year={2025}
}
