Gaze-Based Attention to Improve the Classification of Lung Diseases
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Gaze-Based Attention to Improve the Classification of Lung Diseases. / Kholiavchenko, Maksim; Pershin, Ilya; Maksudov, Bulat; Mustafaev, Tamerlan; Yuan, Yixuan; Ibragimov, Bulat.
Medical Imaging 2022: Image Processing. red. / Olivier Colliot; Ivana Isgum; Bennett A. Landman; Murray H. Loew. SPIE, 2022. 120320C (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Bind 12032).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Gaze-Based Attention to Improve the Classification of Lung Diseases
AU - Kholiavchenko, Maksim
AU - Pershin, Ilya
AU - Maksudov, Bulat
AU - Mustafaev, Tamerlan
AU - Yuan, Yixuan
AU - Ibragimov, Bulat
N1 - Publisher Copyright: © 2022 SPIE
PY - 2022
Y1 - 2022
N2 - Detection of lung diseases from chest X-rays has been of great interest from the research community during the last decade. Despite the existence of large annotated public databases, computer-aided diagnostic solutions still fail on challenging rare abnormality cases. In this study, we investigated the paradigm of combining the analysis of chest X-rays and physician gaze patterns during the analysis of these X-rays to improve the computerized diagnostic accuracy. Tobii Eye Tracker 4C has been mounted to a physician workstation and his eye movements were recorded during the analysis of 400 chest X-rays in two days of work. The X-rays have been sampled from CheXpert, RSNA, and SIIM-ACR public databases labeled with 14 different pathology types. The task was formulated as a binary classification problem. A ResNet34-based neural network has been trained to map the input chest X-ray with the output physician gaze map and binary pathology label. The proposed network improved the diagnostic accuracy to 0.714 of the area under receiving operator curve (AUC) from 0.681 AUC obtained for the same ResNet34 trained to generate binary pathology labels alone. The proposed study has demonstrated the potential benefits of using gaze information in computerized diagnostic solutions.
AB - Detection of lung diseases from chest X-rays has been of great interest from the research community during the last decade. Despite the existence of large annotated public databases, computer-aided diagnostic solutions still fail on challenging rare abnormality cases. In this study, we investigated the paradigm of combining the analysis of chest X-rays and physician gaze patterns during the analysis of these X-rays to improve the computerized diagnostic accuracy. Tobii Eye Tracker 4C has been mounted to a physician workstation and his eye movements were recorded during the analysis of 400 chest X-rays in two days of work. The X-rays have been sampled from CheXpert, RSNA, and SIIM-ACR public databases labeled with 14 different pathology types. The task was formulated as a binary classification problem. A ResNet34-based neural network has been trained to map the input chest X-ray with the output physician gaze map and binary pathology label. The proposed network improved the diagnostic accuracy to 0.714 of the area under receiving operator curve (AUC) from 0.681 AUC obtained for the same ResNet34 trained to generate binary pathology labels alone. The proposed study has demonstrated the potential benefits of using gaze information in computerized diagnostic solutions.
KW - Classification
KW - Deep Learning
KW - Eye-Tracking
KW - Segmentation
U2 - 10.1117/12.2612767
DO - 10.1117/12.2612767
M3 - Article in proceedings
AN - SCOPUS:85131920318
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2022
A2 - Colliot, Olivier
A2 - Isgum, Ivana
A2 - Landman, Bennett A.
A2 - Loew, Murray H.
PB - SPIE
T2 - Medical Imaging 2022: Image Processing
Y2 - 21 March 2021 through 27 March 2021
ER -
ID: 344726785