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. To encourage further research and replication, the code for CPLIP is available on GitHub at https://cplip.github.io/
@article{arxiv.2406.05205,
title = {CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment},
author = {Sajid Javed and Arif Mahmood and Iyyakutti Iyappan Ganapathi and Fayaz Ali Dharejo and Naoufel Werghi and Mohammed Bennamoun},
journal= {arXiv preprint arXiv:2406.05205},
year = {2024}
}