Szczegóły publikacji
Opis bibliograficzny
Automatic quality assessment of reflectance confocal microscopy mosaics using attention-based deep neural network / Marek WODZIŃSKI, Mirosława PAJĄK, Andrzej SKALSKI, Alexander Witkowski, Giovanni Pellacani, Joanna Ludzik // W: EMBC'20 [Dokument elektroniczny] : 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society : "Enabling Innovative Technologies for Global Healthcare" : 20–24 July 2020, Montreal. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, [2020]. — (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; ISSN 1094-687X). — e-ISBN: 978-1-7281-1990-8. — S. 1824–1827. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 1827, Abstr.
Autorzy (6)
- AGHWodziński Marek
- AGHSikorska Mirosława
- AGHSkalski Andrzej
- Witkowski Alexander
- Pellacani Giovanni
- Ludzik Joanna
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 129943 |
|---|---|
| Data dodania do BaDAP | 2020-09-15 |
| Tekst źródłowy | URL |
| DOI | 10.1109/EMBC44109.2020.9176557 |
| Rok publikacji | 2020 |
| 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 2020 |
| Czasopismo/seria | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
Abstract
Skin cancers are the most common cancers with an increased incidence, and a valid, early diagnosis may significantly reduce its morbidity and mortality. Reflectance confocal microscopy (RCM) is a relatively new, non-invasive imaging technique that allows screening lesions at a cellular resolution. However, one of the main disadvantages of the RCM is frequently occurring artifacts which makes the diagnostic process more time consuming and hard to automate using e.g. end-to-end deep learning approach. A tool to automatically determine the RCM mosaic quality could be beneficial for both the lesion classification and informing the user (dermatologist) about its quality in real-time, during the examination procedure. In this work, we propose an attention-based deep network to automatically determine if a given RCM mosaic has an acceptable quality. We achieved accuracy above 87% on the test set which may considerably improve further classification results and the RCM-based examination. © 2020 IEEE.
