Szczegóły publikacji
Opis bibliograficzny
SharpXR: structure-aware denoising for pediatric chest X-rays / Ilerioluwakiiye Abolade, Emmanuel Idoko, Solomon Odelola, Promise Omoigui, Adetola Adebanwo, Aondana Iorumbur, Udunna Anazodo, Alessandro CRIMI, Raymond Confidence // W: Medical Image Computing in Resource Constrained Settings : first international workshop, MIRASOL 2025 held in conjunction with MICCAI 2025 : Daejeon, South Korea, September 27, 2025 : proceedings / eds. Udunna Anazodo, [et al.]. — Cham : Springer, cop. 2026. — ( Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 16398 ). — ISBN: 978-3-032-13653-4; e-ISBN: 978-3-032-13654-1. — S. 83–92. — Bibliogr., Abstr. — Publikacja dostępna online od: 2026-07-02
Autorzy (9)
- Abolade Ilerioluwakiiye
- Idoko Emmanuel
- Odelola Solomon
- Omoigui Promise
- Adebanwo Adetola
- Iorumbur Aondana
- Anazodo Udunna
- AGHCrimi Alessandro
- Confidence Raymond
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 168838 |
|---|---|
| Data dodania do BaDAP | 2026-08-07 |
| DOI | 10.1007/978-3-032-13654-1_9 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | Medical Image Computing and Computer-Assisted Intervention 2025 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Pediatric chest X-ray imaging is essential for early diagnosis, particularly in low-resource settings where advanced imaging modalities are often inaccessible. Low-dose protocols reduce radiation exposure in children but introduce substantial noise that can obscure critical anatomical details. Conventional denoising methods often degrade fine details, compromising diagnostic accuracy. In this paper, we present SharpXR, a structure-aware dual-decoder U-Net designed to denoise low-dose pediatric X-rays while preserving diagnostically relevant features. SharpXR combines a Laplacian-guided edge-preserving decoder with a learnable fusion module that adaptively balances noise suppression and structural detail retention. To address the scarcity of paired training data, we simulate realistic Poisson-Gaussian noise on the Pediatric Pneumonia Chest X-ray dataset. SharpXR outperforms state-of-the-art baselines across all evaluation metrics while maintaining computational efficiency suitable for resource-constrained settings. SharpXR-denoised images improved downstream pneumonia classification accuracy from 88.8% to 92.5%, underscoring its diagnostic value in low-resource pediatric care. Code is available at https://github.com/ileri-oluwa-kiiye/SharpXR.
