| Autor: |
Dimitriou N; Maritime Digitalisation Centre, Cyprus Marine and Maritime Institute, Larnaca 6300, Cyprus.; School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UK., Arandjelović O; School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UK., Harrison DJ; School of Medicine, University of St Andrews, St Andrews KY16 9TF, UK.; NHS Lothian Pathology, Division of Laboratory Medicine, Royal Infirmary of Edinburgh, Edinburgh EH16 4SA, UK. |
| Jazyk: |
angličtina |
| Zdroj: |
Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2024 Mar 01; Vol. 14 (5). Date of Electronic Publication: 2024 Mar 01. |
| DOI: |
10.3390/diagnostics14050524 |
| Abstrakt: |
Amongst the other benefits conferred by the shift from traditional to digital pathology is the potential to use machine learning for diagnosis, prognosis, and personalization. A major challenge in the realization of this potential emerges from the extremely large size of digitized images, which are often in excess of 100,000 × 100,000 pixels. In this paper, we tackle this challenge head-on by diverging from the existing approaches in the literature-which rely on the splitting of the original images into small patches-and introducing magnifying networks (MagNets). By using an attention mechanism, MagNets identify the regions of the gigapixel image that benefit from an analysis on a finer scale. This process is repeated, resulting in an attention-driven coarse-to-fine analysis of only a small portion of the information contained in the original whole-slide images. Importantly, this is achieved using minimal ground truth annotation, namely, using only global, slide-level labels. The results from our tests on the publicly available Camelyon16 and Camelyon17 datasets demonstrate the effectiveness of MagNets-as well as the proposed optimization framework-in the task of whole-slide image classification. Importantly, MagNets process at least five times fewer patches from each whole-slide image than any of the existing end-to-end approaches. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
|
|
Nepřihlášeným uživatelům se plný text nezobrazuje |
K zobrazení výsledku je třeba se přihlásit.
|
