WindowNet: Learnable Windows for Chest X-ray Classification

被引:0
|
作者
Wollek, Alessandro [1 ]
Hyska, Sardi [2 ]
Sabel, Bastian [2 ]
Ingrisch, Michael [2 ]
Lasser, Tobias [1 ]
机构
[1] Tech Univ Munich, Munich Inst Biomed Engn, TUM Sch Computat Informat & Technol, D-80333 Munich, Germany
[2] Ludwig Maximilians Univ Munchen, Dept Radiol, Univ Hosp, D-81377 Munich, Germany
关键词
windowing; chest X-ray; chest radiograph; bit depth; classification; deep learning;
D O I
10.3390/jimaging9120270
中图分类号
TB8 [摄影技术];
学科分类号
0804 ;
摘要
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.
引用
收藏
页数:9
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