  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.
