Foundational Models for Pathology and Endoscopy Images: Application for Gastric Inflammation

• URL: http://arxiv.org/abs/2406.18249v2
• Date: Fri, 30 Aug 2024 14:36:08 GMT
• Title: Foundational Models for Pathology and Endoscopy Images: Application for Gastric Inflammation
• Authors: Hamideh Kerdegari, Kyle Higgins, Dennis Veselkov, Ivan Laponogov, Inese Polaka, Miguel Coimbra, Junior Andrea Pescino, Marcis Leja, Mario Dinis-Ribeiro, Tania Fleitas Kanonnikoff, Kirill Veselkov,
• Abstract summary: Foundation models (FM) are machine or deep learning models trained on diverse data and applicable to broad use cases. FM offer a promising solution to enhance the accuracy of endoscopy and its subsequent pathology image analysis. This review aims to provide a roadmap for researchers and practitioners in navigating the complexities of incorporating FM into clinical practice.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of artificial intelligence (AI) in medical diagnostics represents a significant advancement in managing upper gastrointestinal (GI) cancer, a major cause of global cancer mortality. Specifically for gastric cancer (GC), chronic inflammation causes changes in the mucosa such as atrophy, intestinal metaplasia (IM), dysplasia and ultimately cancer. Early detection through endoscopic regular surveillance is essential for better outcomes. Foundation models (FM), which are machine or deep learning models trained on diverse data and applicable to broad use cases, offer a promising solution to enhance the accuracy of endoscopy and its subsequent pathology image analysis. This review explores the recent advancements, applications, and challenges associated with FM in endoscopy and pathology imaging. We started by elucidating the core principles and architectures underlying these models, including their training methodologies and the pivotal role of large-scale data in developing their predictive capabilities. Moreover, this work discusses emerging trends and future research directions, emphasizing the integration of multimodal data, the development of more robust and equitable models, and the potential for real-time diagnostic support. This review aims to provide a roadmap for researchers and practitioners in navigating the complexities of incorporating FM into clinical practice for prevention/management of GC cases, thereby improving patient outcomes.

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