CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment
<|reference_start|>CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment: 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/<|reference_end|>
@article{javed2024cplip:,
    title={CPLIP: Zero-Shot Learning for Histopathology with Comprehensive
  Vision-Language Alignment},
    author={Sajid Javed, Arif Mahmood, Iyyakutti Iyappan Ganapathi, Fayaz Ali
  Dharejo, Naoufel Werghi, Mohammed Bennamoun},
    journal={arXiv preprint arXiv:2406.05205},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05205},
    primaryClass={cs.CV cs.CL cs.LG cs.MM eess.IV}
}