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

Sajid Javed, Arif Mahmood, Iyyakutti Iyappan Ganapathi, Fayaz Ali Dharejo, Naoufel Werghi, Mohammed Bennamoun

TL;DR

CPLIP addresses the challenge of zero-shot learning in histopathology by enabling unpaired, many-to-many alignment between images and text. It constructs a comprehensive pathology prompt dictionary and builds textual and visual concept bags using MI-Zero, GPT-3, and PLIP, followed by MIL-NCE training to align multiple interrelated concepts. Across tile-level, WSI-level, and segmentation tasks on nine public datasets, CPLIP yields state-of-the-art zero-shot performance compared to existing vision–language approaches, while offering robust interpretability and transferability. The method demonstrates the value of enriched textual prompts and diverse visual content for pathology VL models and is complemented by extensive ablations and supplementary materials to promote reproducibility.

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/

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

TL;DR

CPLIP addresses the challenge of zero-shot learning in histopathology by enabling unpaired, many-to-many alignment between images and text. It constructs a comprehensive pathology prompt dictionary and builds textual and visual concept bags using MI-Zero, GPT-3, and PLIP, followed by MIL-NCE training to align multiple interrelated concepts. Across tile-level, WSI-level, and segmentation tasks on nine public datasets, CPLIP yields state-of-the-art zero-shot performance compared to existing vision–language approaches, while offering robust interpretability and transferability. The method demonstrates the value of enriched textual prompts and diverse visual content for pathology VL models and is complemented by extensive ablations and supplementary materials to promote reproducibility.

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/
Paper Structure (36 sections, 2 equations, 7 figures, 14 tables)

This paper contains 36 sections, 2 equations, 7 figures, 14 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proposed Methodology
  4. Predefined Pathology Dictionary (Fig. \ref{['fig2']} A(a))
  5. Building a Comprehensive Textual Descriptions Bag (Fig. \ref{['fig2']} A(g))
  6. Matching with MI-Zero (Fig. \ref{['fig2']} A(c))
  7. GPT-3 for Comprehensive Textual Descriptions (Fig. \ref{['fig2']} A(d))
  8. Image-to-Text Description with PLIP (Fig.\ref{['fig2']} A(f))
  9. Compiling Visual Concepts Bag (Fig. \ref{['fig2']} B(e))
  10. Image Retrieval with PLIP Based on Textual Prompts (Fig. \ref{['fig2']} B(a)-(b))
  11. PLIP Image-to-Image Retrieval (Fig. \ref{['fig2']} B(c))
  12. Textual and Visual Bags Pruning
  13. MIL-based Contrastive Loss (Fig. \ref{['fig1']} (b))
  14. Zero-shot Transfer for Histology Landscape
  15. Experiments
  16. ...and 21 more sections

Figures (7)

  • Figure 1: Comparative analysis of zero-shot classification performance between the proposed CPLIP algorithm and existing SOTA methods such as BiomedCLIP zhang2023large, PLIP huang2023visual, and MI-Zero lu2023visual. The weighted $F_{1}$ scores demonstrate CPLIP's substantial performance enhancements across six independent histology datasets.
  • Figure 2: (a) Displays the traditional one-to-one alignment in computational pathology VL models like PLIP huang2023visual, BiomedCLIP zhang2023large, and MI-Zero lu2023visual, where each histology image is aligned with a single textual description during fine-tuning. (b) Our proposed approach of many-to-many alignment, where bags of correlated texts are aligned with bags of correlated histology images during fine-tuning, offers a richer, interconnected data set for model training.
  • Figure 3: Diagram outlining the construction of comprehensive textual descriptions and visual concept bags. (A) illustrates the construction process of the textual description bag, while (B) shows the procedure for constructing the visual concept bag. Within (A), there are three primary steps: using MI-Zero to identify the best text match, leveraging GPT-3 to enrich the textual descriptions of the best-matched text, and employing the PLIP text encoder to generate more in-depth descriptions of the input unlabeled histology image. Within (B), there are also three primary steps: (a) using PLIP to identify the best-matching images, (b) leveraging PLIP to enrich the histology images of the best-matched textual descriptions, and (c) employing the PLIP to retrieve relevant histology images of the input unlabeled histology image.
  • Figure 4: Diagram outlining the detailed construction process of the textual description bag ($B_{i}^{t}$) for best-matched prompt, "squamous cell carcinoma" shown in the main paper (Fig. 3 (A)). There are three primary steps: using MI-Zero to identify the best text match, leveraging GPT-3 to enrich the textual descriptions of the best-matched text, and employing the PLIP text encoder to generate more in-depth descriptions of the input unlabeled histology image.
  • Figure 5: This diagram details the steps taken to create the bag of visual concepts $B_{i}^{v}$ for the best-matched prompt "squamous cell carcinoma" shown in the main paper (Fig. 3 (B)). The process involves (a) using PLIP to select images that closely match the prompt, (b) using PLIP to enrich the dataset with histology images that align with the best-matched textual descriptions, and (c) employing PLIP to retrieve relevant histology images for the input unlabeled histology image
  • ...and 2 more figures