Awasthi Akash, Ahmad Safwan, Le Bryant, Nguyen Hien
Electrical and Computer Engineering Department, University of Houston.
Proc IEEE Int Symp Biomed Imaging. 2024 May;2024. doi: 10.1109/isbi56570.2024.10635322. Epub 2024 Aug 22.
In the realm of chest X-ray (CXR) image analysis, radiologists meticulously examine various regions, documenting their observations in reports. The prevalence of errors in CXR diagnoses, particularly among inexperienced radiologists and hospital residents, underscores the importance of understanding radiologists' intentions and the corresponding regions of interest. This understanding is crucial for correcting mistakes by guiding radiologists to the accurate regions of interest, especially in the diagnosis of chest radiograph abnormalities. In response to this imperative, we propose a novel system designed to identify the primary intentions articulated by radiologists in their reports and the corresponding regions of interest in CXR images. This system seeks to elucidate the visual context underlying radiologists' textual findings, with the potential to rectify errors made by less experienced practitioners and direct them to precise regions of interest. Importantly, the proposed system can be instrumental in providing constructive feedback to inexperienced radiologists or junior residents in the hospital, bridging the gap in face-to-face communication. The system represents a valuable tool for enhancing diagnostic accuracy and fostering continuous learning within the medical community.
在胸部X光（CXR）图像分析领域，放射科医生会仔细检查各个区域，并在报告中记录他们的观察结果。CXR诊断中错误的发生率，尤其是在经验不足的放射科医生和医院住院医生中，凸显了理解放射科医生意图以及相应感兴趣区域的重要性。这种理解对于通过引导放射科医生找到准确的感兴趣区域来纠正错误至关重要，特别是在胸部X光片异常诊断中。为应对这一迫切需求，我们提出了一种新颖的系统，旨在识别放射科医生在报告中表达的主要意图以及CXR图像中相应的感兴趣区域。该系统旨在阐明放射科医生文本发现背后的视觉背景，有可能纠正经验不足的从业者所犯的错误，并将他们引导至精确的感兴趣区域。重要的是，所提出的系统有助于为医院中经验不足的放射科医生或初级住院医生提供建设性反馈，弥合面对面交流中的差距。该系统是提高诊断准确性和促进医学社区持续学习的宝贵工具。
