Skip to yearly menu bar Skip to main content


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

Sajid Javed · Arif Mahmood · IYYAKUTTI IYAPPAN GANAPATHI · Fayaz Ali · Naoufel Werghi · Mohammed Bennamoun

Arch 4A-E Poster #174
[ ]
Thu 20 Jun 10:30 a.m. PDT — noon PDT


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 vocabulary, 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 xxx.

Live content is unavailable. Log in and register to view live content