Shedding light on ai in radiology: A systematic review and taxonomy of eye gaze-driven interpretability in deep learning.

作者：José Neves, Chih-Jou Hsieh, Isabel Blanco Nobre, Sandra Costa Sousa, Chun Ouyang, Anderson Maciel, Andrew T. Duchowski, Joaquim A. Jorge, Catarina Moreira · 发表于：European Journal of Radiology · 年份：2024 · DOI：10.1016/j.ejrad.2024.111341 · 被引用次数：22 · 研究领域：Medicine

X-ray imaging plays a crucial role in diagnostic medicine. Yet, a significant portion of the global population lacks access to this essential technology due to a shortage of trained radiologists. Eye-tracking data and deep learning models can enhance X-ray analysis by mapping expert focus areas, guiding automated anomaly detection, optimizing workflow efficiency, and bolstering training methods for novice radiologists. However, the literature shows contradictory results regarding the usefulness of eye-tracking data in deep-learning architectures for abnormality detection. We argue that these discrepancies between studies in the literature are due to (a) the way eye-tracking data is (or is not) processed, (b) the types of deep learning architectures chosen, and (c) the type of application that these architectures will have. We conducted a systematic literature review using PRISMA to address these contradicting results. We analyzed 60 studies that incorporated eye-tracking data in a deep-learning approach for different application goals in radiology. We performed a comparative analysis to understand if eye gaze data contains feature maps that can be useful under a deep learning approach and whether they can promote more interpretable predictions. To the best of our knowledge, this is the first survey in the area that performs a thorough investigation of eye gaze data processing techniques and their impacts in different deep learning architectures for applications such as error ...