Szczegóły publikacji
Opis bibliograficzny
Improving AI interpretability for multilingual Parkinson’s disease classification through voice analysis / Daria HEMMERLING, Michał Zakrzewski, Marek WODZIŃSKI, Miłosz DUDEK, Filip Gąciarz, Magdalena Wójcik-Pędziwiatr, Juan Rafael Orozco-Arroyave, Elmar Noth, David Sztaho, Taras Rumezhak // W: AAAI Bridge Program on AI for Medicine and Healthcare [Dokument elektroniczny] : 25 February 2025, Philadelphia, Pennsylvania, USA : proceedings / eds. Junde Wu, [et al.]. — Wersja do Windows. — Dane tekstowe. — [Cambridge : JMLR], cop. 2025. — ( Proceedings of Machine Learning Research ; ISSN 2640-3498 ; vol. 281 ). — S. [49]–[55]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [54]–[55], Abstr. — D. Hemmerling - dod. afiliacja: SoftServe, Poland
Autorzy (10)
- AGHHemmerling Daria
- Zakrzewski Michał
- AGHWodziński Marek
- AGHDudek Miłosz
- AGHGąciarz Filip
- Wójcik-Pędziwiatr Magdalena
- Orozco-Arroyave Juan Rafael
- Noth Elmar
- Sztaho David
- Rumezhak Taras
Dane bibliometryczne
| ID BaDAP | 159894 |
|---|---|
| Data dodania do BaDAP | 2025-06-10 |
| Tekst źródłowy | URL |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Czasopismo/seria | Proceedings of Machine Learning Research |
Abstract
Addressing the imperative need for interpretability in medical models based on machine learning and artificial intelligence, our study focuses on the crucial task of Parkinson’s disease detection. In this paper, we introduce a vision transformer incorporating multilingual vowel phonations, achieving a classification accuracy of 89%. To enrich the input representation for vision transformer, we utilized images of melspectrograms and regular spectrograms. The success of our model goes beyond performance metrics, as we strategically integrate explainable artificial intelligence techniques. The synergy between robust classification results and explainability underscores the effectiveness of our approach in opening the black-box nature of neural networks. This, in turn, contributes to enhanced medical decision-making and reinforces the potential of artificial intelligence in advancing diagnostic methodologies for Parkinson’s disease.
