Publication details
Úvod do strojového učení pro patology
| Title in English | Introduction to Machine Learning for Pathologists |
|---|---|
| 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/2025-1-14/uvod-do-strojoveho-uceni-pro-patology-140208 |
| Keywords | Decision trees; Diagnostic Systems; Digital Pathology; machine learning; Neural networks |
| Attached files | |
| Description | Digitalization has gradually made its way into many areas of medicine, including pathology. Along with digital data processing comes the application of artifi- cial intelligence methods to simplify routine processes, enhance safety, etc. Although general awareness of artificial intelligence methods is increasing, it is still not common for professionals from non-technical fields to have a detailed understanding of how such systems work and learn. This text aims to explain the basics of machine learning in an accessible way using examples and illustrations from digital pathology. This is not intended to be a comprehensive overview or an introduction to cutting-edge methods. Instead, we use the simplest models to focus on fundamental concepts behind most learning systems. The text concentrates on decision trees, whose functionality is easy to explain, and basic neural networks, the primary models used in today’s artificial intelligence. We also attempt to describe the collaborative process between medical specialists, who provide the data, and computer scientists, who use this data to develop learning systems. This text will help bridge the knowledge gap between medical professionals and computer scientists, contributing to more effective interdis- ciplinary collaboration. |