Publication details
Strojové učení v digitální patologii
| Title in English | Machine learning in digital pathology |
|---|---|
| Authors | |
| Year of publication | 2025 |
| Type | Peer-reviewed scientific article |
| Magazine / Source | Ceskoslovenska Patologie |
| MU Faculty or unit | |
| Citation | |
| web | https://www.prolekare.cz/en/journals/czecho-slovak-pathology/2025-2-16/strojove-uceni-v-digitalni-patologii-141102 |
| Keywords | artificial intelligence; Digital Pathology; Image processing; machine learning; Whole-slide images |
| Attached files | |
| Description | With the advancing digitalization of pathology, the application of machine learning and artificial intelligence methods is becoming increasingly important. Research and development in this field are progressing rapidly, but the clinical implementation of learning systems still lags behind. The aim of this text is to provide an overview of the process of developing and deploying learning systems in digital pathology. We begin by describing the fundamental characteristics of data produced in digital pathology. Specifically, we discuss scanners and sample scanning, data storage and transmission, quality control, and preparation for processing by learning systems, with a particular focus on annotations. Our goal is to present current approaches to addressing technical challenges while also highlighting potential pitfalls in processing digital pathology data. In the first part of the text, we also outline existing software solutions for viewing scanned samples and implementing diagnostic procedures that incorporate learning systems. In the second part of the text, we describe common tasks in digital pathology and outline typical approaches to solving them. Here, we explain the necessary modifications to standard machine learning methods for processing large scans and discuss specific diagnostic applications. Finally, we provide a brief overview of the potential future development of learning systems in digital pathology. We illustrate the transition to large foundational models and introduce the topic of virtual staining of samples. We hope that this text will contribute to a better understanding of the rapidly evolving field of machine learning in digital pathology and, in turn, facilitate the faster adoption of learning-based methods in this domain. |