Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Chest X-ray visual saliency modeling: eye-tracking dataset and saliency prediction model

Lou, Jianxun, Wang, Huasheng, Wu, Xinbo, Cho Hui Ng, John, White, Richard, Thakoor, Kaveri A., Corcoran, Padraig ORCID: https://orcid.org/0000-0001-9731-3385, Chen, Ying and Liu, Hantao ORCID: https://orcid.org/0000-0003-4544-3481 2025. Chest X-ray visual saliency modeling: eye-tracking dataset and saliency prediction model. IEEE Transactions on Neural Networks and Learning Systems 10.1109/tnnls.2025.3564292

Full text not available from this repository.

Abstract

Radiologists’ eye movements during medical image interpretation reflect their perceptual-cognitive processes of diagnostic decisions. The eye movement data can be modeled to represent clinically relevant regions in a medical image and potentially integrated into an artificial intelligence (AI) system for automatic diagnosis in medical imaging. In this article, we first conduct a large-scale eye-tracking study involving 13 radiologists interpreting 191 chest X-ray (CXR) images, establishing a best-of-its-kind CXR visual saliency benchmark. We then perform analysis to quantify the reliability and clinical relevance of saliency maps (SMs) generated for CXR images. We develop CXR image saliency prediction method (CXRSalNet), a novel saliency prediction model that leverages radiologists’ gaze information to optimize the use of unlabeled CXR images, enhancing training and mitigating data scarcity. We also demonstrate the application of our CXR saliency model in enhancing the performance of AI-powered diagnostic imaging systems.

Item Type: Article
Date Type: Published Online
Status: In Press
Schools: Schools > Computer Science & Informatics
Additional Information: License information from Publisher: LICENSE 1: URL: https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html, Start Date: 2025-01-01
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 2162-237X
Last Modified: 04 Jun 2025 08:45
URI: https://orca.cardiff.ac.uk/id/eprint/178735

Actions (repository staff only)

Edit Item Edit Item