WindowNet: Learnable Windows for Chest X-ray Classification

Autor:	Alessandro Wollek, Sardi Hyska, Bastian Sabel, Michael Ingrisch, Tobias Lasser
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	windowing chest X-ray chest radiograph bit depth classification deep learning Photography TR1-1050 Computer applications to medicine. Medical informatics R858-859.7 Electronic computers. Computer science QA75.5-76.95
Zdroj:	Journal of Imaging, Vol 9, Iss 12, p 270 (2023)
Druh dokumentu:	article
ISSN:	2313-433X 04042824
DOI:	10.3390/jimaging9120270
Popis:	Public chest X-ray (CXR) data sets are commonly compressed to a lower bit depth to reduce their size, potentially hiding subtle diagnostic features. In contrast, radiologists apply a windowing operation to the uncompressed image to enhance such subtle features. While it has been shown that windowing improves classification performance on computed tomography (CT) images, the impact of such an operation on CXR classification performance remains unclear. In this study, we show that windowing strongly improves the CXR classification performance of machine learning models and propose WindowNet, a model that learns multiple optimal window settings. Our model achieved an average AUC score of 0.812 compared with the 0.759 score of a commonly used architecture without windowing capabilities on the MIMIC data set.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/c0ea9511f4f04042824141f5e31878cc Zobrazit plný text záznamu View record in DOAJ