Pathology Foundation Models

Table of Contents

Title: Pathology Foundation Models

Authors: Mieko Ochi, Daisuke Komura, Shumpei Ishikawa

Published: Jul 31 2024

Link: https://arxiv.org/abs/2407.21317

Summary (Generated by Microsoft Copilot):

  • Introduction: The paper discusses the role of pathology in diagnosing and evaluating patient tissue samples, highlighting advancements in digital pathology and AI, particularly Foundation Models (FMs).

  • Challenges: Key challenges include the high annotation cost, lack of public datasets, hardware requirements, potential domain shifts, and the need for regular updates to prevent inaccuracies.

  • Methods: The study utilizes Whole Slide Scanners and deep learning technologies to develop pathology AI models, focusing on large-scale AI models known as Foundation Models.

  • Novelties: The emergence of Generalist Medical AI, integrating pathology FMs with other medical domains, and the development of multimodal FMs that handle various data modalities.

  • Results: Pathology FMs have shown potential in tasks like disease diagnosis, prognosis prediction, and biomarker expression prediction, with some models achieving state-of-the-art performance.

  • Performances: Comparative accuracy experiments show that models like UNI and Prov-GigaPath perform well in disease detection, biomarker prediction, and treatment outcome prediction.

  • Limitations: Issues include the “black box” nature of AI, potential hallucinations, and insufficient validation in real clinical settings.

  • Discussion: Future directions involve optimizing models for less resource-intensive hardware, developing explainable AI, and ensuring clinical safety and effective deployment of FMs.