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

TL;DR

CPLIP introduces a comprehensive unsupervised vision-language pre-training approach tailored for histopathology, significantly improving zero-shot classification and segmentation by enhancing image-text alignment without requiring annotated data.

Contribution

The paper presents a novel unsupervised pre-training framework that constructs pathology-specific dictionaries and uses contrastive learning to align complex image-text concepts in histopathology.

Findings

Outperforms existing methods in zero-shot histopathology tasks

Enhances interpretability and robustness of vision-language models

Sets new benchmarks in histopathology image analysis

Abstract

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/