CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment

作者：Sajid Javed, Arif Mahmood, I. I. Ganapathi, Fayaz Ali Dharejo, N. Werghi, Mohammed Bennamoun · 发表于：Computer Vision and Pattern Recognition · 年份：2024 · DOI：10.1109/cvpr52733.2024.01088 · 被引用次数：43 · 研究领域：Computer Science、Engineering

This paper proposes Comprehensive Pathology Language Image Pretraining (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 pretrained 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/