Szczegóły publikacji
Opis bibliograficzny
Artifact augmentation for learning-based quality control of whole slide images / Artur JURGAS, Marek WODZIŃSKI, Weronika CELNIAK, Manfredo Atzori, Henning Müller // W: EMBC 2023 [Dokument elektroniczny] : 2023 45th annual international conference of the IEEE Engineering in Medicine & Biology Conference : Sydney, Australia, 24-27 July 2023 : proceedings / IEEE. — Wersja do Windows. — Dane tekstowe. — [Piscataway, NJ, USA] : IEEE, cop. 2023. — (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; ISSN 1094-687X). — e-ISBN: 979-8-3503-2447-1. — S. [1–4]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [4], Abstr. — A. Jurgas, M. Wodziński, W. Celniak – dod. afiliacja: University of Applied Sciences Western Switzerland (HES-SO Valais), Switzerland
Autorzy (5)
- AGHJurgas Artur
- AGHWodziński Marek
- AGHCelniak Weronika
- Atzori Manfredo
- Müller Henning
Dane bibliometryczne
| ID BaDAP | 151015 |
|---|---|
| Data dodania do BaDAP | 2024-01-05 |
| Tekst źródłowy | URL |
| DOI | 10.1109/EMBC40787.2023.10340997 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | The Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2023 |
| Czasopismo/seria | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
Abstract
The acquisition of whole slide images is prone to artifacts that can require human control and re-scanning, both in clinical workflows and in research-oriented settings. Quality control algorithms are a first step to overcome this challenge, as they limit the use of low quality images. Developing quality control systems in histopathology is not straightforward, also due to the limited availability of data related to this topic. We address the problem by proposing a tool to augment data with artifacts. The proposed method seamlessly generates and blends artifacts from an external library to a given histopathology dataset. The datasets augmented by the blended artifacts are then used to train an artifact detection network in a supervised way. We use the YOLOv5 model for the artifact detection with a slightly modified training pipeline. The proposed tool can be extended into a complete framework for the quality assessment of whole slide images.Clinical relevance— The proposed method may be useful for the initial quality screening of whole slide images. Each year, millions of whole slide images are acquired and digitized worldwide. Numerous of them contain artifacts affecting the following AI-oriented analysis. Therefore, a tool operating at the acquisition phase and improving the initial quality assessment is crucial to increase the performance of digital pathology algorithms, e.g., early cancer diagnosis.
