Explainable AI for Meniscus Segmentation on MRI
Fragment książki (Rozdział monografii pokonferencyjnej)
MNiSW
20
Poziom I
| Status: | |
| Autorzy: | Kumarkanova Akbota, Tlebaldinova Aizhan, Kumargazhanova Saule, Karmenova Markhaba, Omiotek Zbigniew |
| Dyscypliny: | |
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| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Strony: | 1 - 5 |
| Bazy: | IEEE Xplore |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | 18th International Conference on Electronics, Computer, and Computation |
| Skrócona nazwa konferencji: | 18th ICECCO 2026 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 10 kwietnia 2026 do 11 kwietnia 2026 |
| Miasto konferencji: | Kaskelen |
| Państwo konferencji: | KAZACHSTAN |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| Meniscus tears are among the most common injuries of the knee joint and require accurate and timely diagnosis. Deep learning–based segmentation models are widely used to automate the analysis of medical images; however, the limited interpretability of their predictions necessitates the application of explainable artificial intelligence (XAI) methods. In this study, the Grad-CAM and RISE methods are compared for the explainable interpretation of meniscus tear segmentation in magnetic resonance imaging (MRI) using the YOLOv8m-seg model. The study was conducted on a clinical dataset comprising 4000 2D MRI slices (normal/tear) with manually annotated segmentation masks. The evaluation of the methods is based on the Insertion and Deletion metrics, as well as on visual analysis of the explanation maps on the test set. The results demonstrate the superiority of the RISE method in generating more localized explanations supported by quantitative evaluation, thereby confirming the potential of model-agnostic XAI approaches for medical image analysis. |
