@article{f196738f97c8499db82cb97e277b7635,
title = "GUI System to Support Cardiology Examination Based on Explainable Regression CNN for Estimating Pulmonary Artery Wedge Pressure",
abstract = "In this article, a GUI system is proposed to support clinical cardiology examinations. The proposed system estimates {"}pulmonary artery wedge pressure{"}based on patients' chest radiographs using an explainable regression-based convolutional neural network. The GUI system was validated by performing an effectiveness survey with 23 cardiology physicians with medical licenses. The results indicated that many physicians considered the GUI system to be effective.",
keywords = "cardiology examination, convolutional neural network, pulmonary artery wedge pressure, regression activation map",
author = "Yuto Omae and Yuki Saito and Yohei Kakimoto and Daisuke Fukamachi and Koichi Nagashima and Yasuo Okumura and Jun Toyotani",
note = "Publisher Copyright: {\textcopyright} 2023 The Institute of Electronics.",
year = "2023",
month = mar,
doi = "10.1587/transinf.2022EDL8059",
language = "English",
volume = "E106D",
pages = "423--426",
journal = "IEICE Transactions on Information and Systems",
issn = "0916-8532",
publisher = "Maruzen Co., Ltd/Maruzen Kabushikikaisha",
number = "3",
}