Contrastive Learning Approach to Predict Radiologist's Error Based on Gaze Data

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Standard

Contrastive Learning Approach to Predict Radiologist's Error Based on Gaze Data. / Pershin, Ilya; Mustafaev, Tamerlan; Ibragimov, Bulat.

2023 IEEE Congress on Evolutionary Computation, CEC 2023. IEEE, 2023.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Pershin, I, Mustafaev, T & Ibragimov, B 2023, Contrastive Learning Approach to Predict Radiologist's Error Based on Gaze Data. i 2023 IEEE Congress on Evolutionary Computation, CEC 2023. IEEE, 2023 IEEE Congress on Evolutionary Computation, CEC 2023, Chicago, USA, 01/07/2023. https://doi.org/10.1109/CEC53210.2023.10254056

APA

Pershin, I., Mustafaev, T., & Ibragimov, B. (2023). Contrastive Learning Approach to Predict Radiologist's Error Based on Gaze Data. I 2023 IEEE Congress on Evolutionary Computation, CEC 2023 IEEE. https://doi.org/10.1109/CEC53210.2023.10254056

Vancouver

Pershin I, Mustafaev T, Ibragimov B. Contrastive Learning Approach to Predict Radiologist's Error Based on Gaze Data. I 2023 IEEE Congress on Evolutionary Computation, CEC 2023. IEEE. 2023 https://doi.org/10.1109/CEC53210.2023.10254056

Author

Pershin, Ilya ; Mustafaev, Tamerlan ; Ibragimov, Bulat. / Contrastive Learning Approach to Predict Radiologist's Error Based on Gaze Data. 2023 IEEE Congress on Evolutionary Computation, CEC 2023. IEEE, 2023.

Bibtex

@inproceedings{2ae12f1ab0f54f3080283e80707e8059,
title = "Contrastive Learning Approach to Predict Radiologist's Error Based on Gaze Data",
abstract = "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.",
keywords = "deep learning, eye-tracking, human-AI interaction, self-supervised",
author = "Ilya Pershin and Tamerlan Mustafaev and Bulat Ibragimov",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Congress on Evolutionary Computation, CEC 2023 ; Conference date: 01-07-2023 Through 05-07-2023",
year = "2023",
doi = "10.1109/CEC53210.2023.10254056",
language = "English",
booktitle = "2023 IEEE Congress on Evolutionary Computation, CEC 2023",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Contrastive Learning Approach to Predict Radiologist's Error Based on Gaze Data

AU - Pershin, Ilya

AU - Mustafaev, Tamerlan

AU - Ibragimov, Bulat

N1 - Publisher Copyright: © 2023 IEEE.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - deep learning

KW - eye-tracking

KW - human-AI interaction

KW - self-supervised

UR - http://www.scopus.com/inward/record.url?scp=85174491896&partnerID=8YFLogxK

U2 - 10.1109/CEC53210.2023.10254056

DO - 10.1109/CEC53210.2023.10254056

M3 - Article in proceedings

AN - SCOPUS:85174491896

BT - 2023 IEEE Congress on Evolutionary Computation, CEC 2023

PB - IEEE

T2 - 2023 IEEE Congress on Evolutionary Computation, CEC 2023

Y2 - 1 July 2023 through 5 July 2023

ER -

ID: 372615699