A Minimally Supervised Approach for Medical Image Quality Assessment in Domain Shift Settings

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A Minimally Supervised Approach for Medical Image Quality Assessment in Domain Shift Settings
Title:
A Minimally Supervised Approach for Medical Image Quality Assessment in Domain Shift Settings
Journal Title:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publication Date:
27 April 2022
Citation:
Yang, H., Coyner, A. S., Guretno, F., Mien, I. H., Sheng Foo, C., Campbell, J. P., Ostmo, S., Chiang, M. F., & Krishnaswamy, P. (2022). A Minimally Supervised Approach for Medical Image Quality Assessment in Domain Shift Settings. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp43922.2022.9746837
Abstract:
Accurate disease diagnosis requires objective assessment of clinical image quality. Automated image quality assessment (IQA) could enhance screening and diagnosis workflows. However, development of generalizable quality assessment tools requires large labeled clinical image datasets from different sites. Obtaining these datasets is often infeasible; and quality indicators may vary with acquisition settings due to domain shift. We introduce a minimally-supervised image quality assessment (MIQA) approach that can learn effectively with small datasets and limited labels in class-imbalanced domain shift scenarios. We formulate the IQA task as an anomaly detection problem, and use a small number of target domain images to identify a compact subset of source domain data for better representation of acceptable quality features. For this compact source domain dataset, we extract features with a pre-trained CNN, perform adaptive feature selection, and develop a one-class classifier to detect poor quality images. We evaluate our approach on two ophthalmology datasets, and show substantial AUC gains and improved cross-site generalizability over competitive baselines. Our approach has implications for improved image quality audit in many clinical settings.
License type:
Publisher Copyright
Funding Info:
This research is supported by core funding from: Institute for Infocomm Research, A*STAR
Grant Reference no. : EC-2018-046
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2379-190X
1520-6149
ISBN:
978-1-6654-0540-9
978-1-6654-0541-6
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