Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

• URL: http://arxiv.org/abs/2502.08333v1
• Date: Wed, 12 Feb 2025 11:57:11 GMT
• Title: Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact
• Authors: Mohsin Bilal, Aadam, Manahil Raza, Youssef Altherwy, Anas Alsuhaibani, Abdulrahman Abduljabbar, Fahdah Almarshad, Paul Golding, Nasir Rajpoot,
• Abstract summary: Generative AI "co-pilots" now demonstrate the ability to mine subtle, sub-visual tissue cues across the cellular-to-pathology spectrum. The scale of data has surged dramatically, growing from tens to millions of multi-gigapixel tissue images. We explore the true potential of these innovations and their integration into clinical practice.
• Score: 0.34826922265324145
• License:
• Abstract: From self-supervised, vision-only models to contrastive visual-language frameworks, computational pathology has rapidly evolved in recent years. Generative AI "co-pilots" now demonstrate the ability to mine subtle, sub-visual tissue cues across the cellular-to-pathology spectrum, generate comprehensive reports, and respond to complex user queries. The scale of data has surged dramatically, growing from tens to millions of multi-gigapixel tissue images, while the number of trainable parameters in these models has risen to several billion. The critical question remains: how will this new wave of generative and multi-purpose AI transform clinical diagnostics? In this article, we explore the true potential of these innovations and their integration into clinical practice. We review the rapid progress of foundation models in pathology, clarify their applications and significance. More precisely, we examine the very definition of foundational models, identifying what makes them foundational, general, or multipurpose, and assess their impact on computational pathology. Additionally, we address the unique challenges associated with their development and evaluation. These models have demonstrated exceptional predictive and generative capabilities, but establishing global benchmarks is crucial to enhancing evaluation standards and fostering their widespread clinical adoption. In computational pathology, the broader impact of frontier AI ultimately depends on widespread adoption and societal acceptance. While direct public exposure is not strictly necessary, it remains a powerful tool for dispelling misconceptions, building trust, and securing regulatory support.

Related papers

• From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine [40.23383597339471]
  multimodal AI is capable of integrating diverse data modalities, including imaging, text, and structured data, within a single model. This scoping review explores the evolution of multimodal AI, highlighting its methods, applications, datasets, and evaluation in clinical settings. Our findings underscore a shift from unimodal to multimodal approaches, driving innovations in diagnostic support, medical report generation, drug discovery, and conversational AI.
  arXiv  Detail & Related papers  (2025-02-13T11:57:51Z)
• Foundation Models in Radiology: What, How, When, Why and Why Not [15.314933454351674]
  Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Foundation models have recently received extensive attention from academic, industry, and regulatory bodies. This review aims to establish a standardized terminology concerning foundation models.
  arXiv  Detail & Related papers  (2024-11-27T20:13:01Z)
• CanvOI, an Oncology Intelligence Foundation Model: Scaling FLOPS Differently [0.0]
  We present CanvOI, a ViT-g/10-based foundation model designed to enhance the capabilities of digital pathology. By introducing larger tile sizes (380 x 380 pixels) and smaller patch sizes (10 x 10 pixels), we were able to optimize the model's performance.
  arXiv  Detail & Related papers  (2024-09-04T17:15:44Z)
• 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)
• On the Challenges and Opportunities in Generative AI [135.2754367149689]
  We argue that current large-scale generative AI models do not sufficiently address several fundamental issues that hinder their widespread adoption across domains. In this work, we aim to identify key unresolved challenges in modern generative AI paradigms that should be tackled to further enhance their capabilities, versatility, and reliability.
  arXiv  Detail & Related papers  (2024-02-28T15:19:33Z)
• Demographic Bias of Expert-Level Vision-Language Foundation Models in Medical Imaging [13.141767097232796]
  Self-supervised vision-language foundation models can detect a broad spectrum of pathologies without relying on explicit training annotations. It is crucial to ensure that these AI models do not mirror or amplify human biases, thereby disadvantaging historically marginalized groups such as females or Black patients. This study investigates the algorithmic fairness of state-of-the-art vision-language foundation models in chest X-ray diagnosis across five globally-sourced datasets.
  arXiv  Detail & Related papers  (2024-02-22T18:59:53Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models [49.95603725998561]
  We propose a new paradigm to build robust and interpretable medical image classifiers with natural language concepts. Specifically, we first query clinical concepts from GPT-4, then transform latent image features into explicit concepts with a vision-language model.
  arXiv  Detail & Related papers  (2023-10-04T21:57:09Z)
• Safe AI for health and beyond -- Monitoring to transform a health service [51.8524501805308]
  We will assess the infrastructure required to monitor the outputs of a machine learning algorithm. We will present two scenarios with examples of monitoring and updates of models.
  arXiv  Detail & Related papers  (2023-03-02T17:27:45Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

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.