Foundation Models in Digital Pathology Imaging: Next-Generation AI for Healthcare Transformation

Abstract

Digital pathology has revolutionized the field of pathology by enabling the digitization of glass slides into highresolution whole slide images (WSIs), facilitating seamless storage, sharing, and analysis of histopathological data. This shift from conventional microscopy to digital platforms has opened new possibilities for computational pathology, allowing the application of artificial intelligence (AI) to assist in diagnosis, prognosis, and treatment planning. Pathology is critical in diagnosing and evaluating patient tissue samples, where accuracy and efficiency are paramount. The advent of deep learning and AI technologies has significantly advanced digital pathology by enhancing diagnostic precision, reducing pathologists’ workload, and streamlining decision-making processes. Among AI advancements, foundation models (FMs) have gained significant prominence, offering superior performance over traditional machine learning methods. These models have shown remarkable capabilities in various pathology tasks, including disease diagnosis, rare cancer detection, survival prognosis, and biomarker expression prediction. Despite their promising performance, the clinical application of FMs faces several challenges, such as data quality, interpretability, scalability, and regulatory compliance. This study explores FMs in digital pathology, including their underlying architectures, such as transformers, convolutional neural networks (CNNs), and self-supervised learning approaches. It provides an in-depth analysis of prominent models like Titan, CONCH, and qPath. The review highlights their applications in tumor classification, tissue segmentation, and biomarker detection, while addressing key challenges and opportunities for future research.

Ähnliche Arbeiten