Contrastive Learning Approach to Predict Radiologist's Error Based on Gaze Data
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
The increase in medical imaging and, consequently, the growing workload of radiologists requires workflow optimization. The interaction between radiologists and artificial intelligence is a promising area because it can improve radiologist productivity and reduce diagnostic errors. In this study, we describe an eye-tracking experiment with four practicing radiologists who cumulatively examined 1,000 unique chest X-ray images that included samples with multiple pathologies. We demonstrate the feasibility of a self-supervised approach for gazebased data for problems with a small amount of labeled data. We achieve an accuracy of 64.7% for binary classification of radiologist diagnostic error with 218 samples per label. The proposed approach can be used in the system for assessing the need for a second opinion to improve the quality of diagnostics.
Originalsprog | Engelsk |
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Titel | 2023 IEEE Congress on Evolutionary Computation, CEC 2023 |
Forlag | IEEE |
Publikationsdato | 2023 |
ISBN (Elektronisk) | 9798350314588 |
DOI | |
Status | Udgivet - 2023 |
Begivenhed | 2023 IEEE Congress on Evolutionary Computation, CEC 2023 - Chicago, USA Varighed: 1 jul. 2023 → 5 jul. 2023 |
Konference
Konference | 2023 IEEE Congress on Evolutionary Computation, CEC 2023 |
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Land | USA |
By | Chicago |
Periode | 01/07/2023 → 05/07/2023 |
Bibliografisk note
Funding Information:
This research has been financially supported by The Analytical Center for the Government of the Russian Federation (Agreement No. 70-2021-00143 dd. 01.11.2021, IGK 000000D730321P5Q0002).
Publisher Copyright:
© 2023 IEEE.
ID: 372615699