Pathology Foundation Models

• URL: http://arxiv.org/abs/2407.21317v2
• Date: Tue, 6 Aug 2024 05:42:42 GMT
• Title: Pathology Foundation Models
• Authors: Mieko Ochi, Daisuke Komura, Shumpei Ishikawa,
• Abstract summary: Development of deep learning technologies have led to extensive research and development in pathology AI (Artificial Intelligence) Large-scale AI models known as Foundation Models (FMs) have emerged and expanded their application scope in the healthcare field. FMs are more accurate and applicable to a wide range of tasks compared to traditional AI.
• Score: 0.0354287905099182
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pathology has played a crucial role in the diagnosis and evaluation of patient tissue samples obtained from surgeries and biopsies for many years. The advent of Whole Slide Scanners and the development of deep learning technologies have significantly advanced the field, leading to extensive research and development in pathology AI (Artificial Intelligence). These advancements have contributed to reducing the workload of pathologists and supporting decision-making in treatment plans. Recently, large-scale AI models known as Foundation Models (FMs), which are more accurate and applicable to a wide range of tasks compared to traditional AI, have emerged, and expanded their application scope in the healthcare field. Numerous FMs have been developed in pathology, and there are reported cases of their application in various tasks, such as disease diagnosis, rare cancer diagnosis, patient survival prognosis prediction, biomarker expression prediction, and the scoring of immunohistochemical expression intensity. However, several challenges remain for the clinical application of FMs, which healthcare professionals, as users, must be aware of. Research is ongoing to address these challenges. In the future, it is expected that the development of Generalist Medical AI, which integrates pathology FMs with FMs from other medical domains, will progress, leading to the effective utilization of AI in real clinical settings to promote precision and personalized medicine.

Related papers

• AI in Oncology: Transforming Cancer Detection through Machine Learning and Deep Learning Applications [0.3937575566991286]
  This review examines the limitations of conventional diagnostic techniques and explores the transformative role of AI in diagnosing and treating cancers. The study delves into the application of AI in radiomics for detailed cancer characterization, predictive analytics for identifying associated risks, and the development of algorithm-driven robots for immediate diagnosis. The overarching goal of this platform is to support the development of expert recommendations and to provide universal, efficient diagnostic procedures.
  arXiv  Detail & Related papers  (2025-01-26T11:32:43Z)
• Explainable Artificial Intelligence for Medical Applications: A Review [42.33274794442013]
  This article reviews recent research grounded in explainable artificial intelligence (XAI) It focuses on medical practices within the visual, audio, and multimodal perspectives. We endeavour to categorise and synthesise these practices, aiming to provide support and guidance for future researchers and healthcare professionals.
  arXiv  Detail & Related papers  (2024-11-15T11:31:06Z)
• The Era of Foundation Models in Medical Imaging is Approaching : A Scoping Review of the Clinical Value of Large-Scale Generative AI Applications in Radiology [0.0]
  Social problems stemming from the shortage of radiologists are intensifying, and artificial intelligence is being highlighted as a potential solution. Recently emerging large-scale generative AI has expanded from large language models (LLMs) to multi-modal models. This scoping review systematically organizes existing literature on the clinical value of large-scale generative AI applications.
  arXiv  Detail & Related papers  (2024-09-03T00:48:50Z)
• MGH Radiology Llama: A Llama 3 70B Model for Radiology [50.42811030970618]
  This paper presents an advanced radiology-focused large language model: MGH Radiology Llama. It is developed using the Llama 3 70B model, building upon previous domain-specific models like Radiology-GPT and Radiology-Llama2. Our evaluation, incorporating both traditional metrics and a GPT-4-based assessment, highlights the enhanced performance of this work over general-purpose LLMs.
  arXiv  Detail & Related papers  (2024-08-13T01:30:03Z)
• Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports [51.45762396192655]
  Multimodal large language models (MLLMs) have recently transformed many domains, significantly affecting the medical field. Notably, Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models have epitomized a paradigm shift in Artificial General Intelligence for computer vision. This study evaluated the performance of the Gemini, GPT-4, and 4 popular large models for an exhaustive evaluation across 14 medical imaging datasets.
  arXiv  Detail & Related papers  (2024-07-08T09:08:42Z)
• Foundational Models for Pathology and Endoscopy Images: Application for Gastric Inflammation [0.0]
  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.
  arXiv  Detail & Related papers  (2024-06-26T10:51:44Z)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• Progress and Opportunities of Foundation Models in Bioinformatics [77.74411726471439]
  Foundations models (FMs) have ushered in a new era in computational biology, especially in the realm of deep learning. Central to our focus is the application of FMs to specific biological problems, aiming to guide the research community in choosing appropriate FMs for their research needs. Review analyses challenges and limitations faced by FMs in biology, such as data noise, model explainability, and potential biases.
  arXiv  Detail & Related papers  (2024-02-06T02:29:17Z)
• RudolfV: A Foundation Model by Pathologists for Pathologists [13.17203220753175]
  We present a novel approach to designing foundation models for computational pathology. Our model "RudolfV" surpasses existing state-of-the-art foundation models across different benchmarks.
  arXiv  Detail & Related papers  (2024-01-08T18:31:38Z)
• The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation [81.72197368690031]
  We present a new benchmarking suite designed specifically for medical sequential decision making. The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
  arXiv  Detail & Related papers  (2021-06-08T10:38:09Z)
• Mapping the Landscape of Artificial Intelligence Applications against COVID-19 [59.30734371401316]
  COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization. We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
  arXiv  Detail & Related papers  (2020-03-25T12:30:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.