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

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Pershin, I, Mustafaev, T & Ibragimov, B 2023, Contrastive Learning Approach to Predict Radiologist's Error Based on Gaze Data. in 2023 IEEE Congress on Evolutionary Computation, CEC 2023. IEEE, 2023 IEEE Congress on Evolutionary Computation, CEC 2023, Chicago, United States, 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. In 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. In 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