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

Table of Contents

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

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

Published: Jun 7, 2024

Link: https://arxiv.org/abs/2406.05205v1

Summary (Generated by Microsoft Copilot):

Introduction:

  • The paper introduces Comprehensive Pathology Language Image Pre-training (CPLIP), an unsupervised technique to enhance image-text alignment in histopathology.

Challenges:

  • Scarcity of Whole Slide Images (WSIs) and diverse cancer morphologies.
  • Zero-shot transfer limitations in VL models for tasks like tissue recognition and cancer subtyping.

Methods:

  • Pathology-specific dictionary creation.
  • Textual descriptions generation using language models.
  • Many-to-many contrastive learning for aligning images and text.

Novelties:

  • Comprehensive textual prompts and visual concepts alignment.
  • Enhanced zero-shot learning capabilities.

Results:

  • Improved performance in zero-shot learning scenarios.
  • Outperformed existing methods in interpretability and robustness.

Performances:

  • Superior zero-shot classification and segmentation across multiple datasets.

Limitations:

  • Computational demands due to extensive data processing.

Discussion:

  • Potential for further research and replication.
  • Code availability on GitHub for community use.