This paper proposes Comprehensive Pathology Language Image Pre-training (CPLIP), a new unsupervised technique designed to enhance the alignment of images and text in histopathology for tasks such as classification and segmentation. This methodology enriches vision-language models by leveraging extensive data without needing ground truth annotations. CPLIP involves constructing a pathology-specific dictionary, generating textual descriptions for images using language models, and retrieving relevant images for each text snippet via a pre-trained model. The model is then fine-tuned using a many-to-many contrastive learning method to align complex interrelated concepts across both modalities. Evaluated across multiple histopathology tasks, CPLIP shows notable improvements in zero-shot learning scenarios, outperforming existing methods in both interpretability and robustness and setting a higher benchmark for the application of vision-language models in the field.
- 2024-03-20: Initial code/data release.
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Install Python environment:
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Install requirements.
If you use CPLIP in your research, please cite the following:
@misc{sajid2024cplip,
title={CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment},
author={Sajid Javed, Arif Mahmood, Iyyakutti Iyappan Ganapathi, Fayaz Ali Dharejo1, Naoufel Werghi, Mohammed Bennamoun},
year={2024},
}
This repository borrows heavily from open-clip, Plip and TiMM's library. Special thanks to the contributors of merlot.
The code and pretrained models are provided under the MIT license. See the LICENSE file for details.