Abstract
To maintain a standard in a medical imaging study, images should have necessary image quality for potential diagnostic use. Although CNN-based approaches are used to assess the image quality, their performance can still be improved in terms of accuracy. In this work, we approach this problem by using Swin Transformer, which improves the poor-quality image classification performance that causes the degradation in medical image quality. We test our approach on Foreign Object Classification problem on Chest X-Rays (Object-CXR) and Left Ventricular Outflow Tract Classification problem on Cardiac MRI with a four-chamber view (LVOT). While we obtain a classification accuracy of 87.1% and 95.48% on the Object-CXR and LVOT datasets, our experimental results suggest that the use of Swin Transformer improves the Object-CXR classification performance while obtaining a comparable performance for the LVOT dataset. To the best of our knowledge, our study is the first vision transformer application for medical image quality assessment.
Original language | English |
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Title of host publication | Machine Learning in Medical Imaging - 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Proceedings |
Editors | Chunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Zhiming Cui |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 425-435 |
Number of pages | 11 |
ISBN (Print) | 9783031210136 |
DOIs | |
Publication status | Published - 2022 |
Event | 13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer_Assisted Intervention, MICCAI 2022 - Singapore, Singapore Duration: 18 Sept 2022 → 18 Sept 2022 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13583 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer_Assisted Intervention, MICCAI 2022 |
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Country/Territory | Singapore |
City | Singapore |
Period | 18/09/22 → 18/09/22 |
Bibliographical note
Publisher Copyright:© 2022, Springer Nature Switzerland AG.
Funding
Acknowledgments. This paper has been produced benefiting from the 2232 International Fellowship for Outstanding Researchers Program of TUBITAK (Project No: 118C353). However, the entire responsibility of the publication/paper belongs to the owner of the paper. The financial support received from TUBITAK does not mean that the content of the publication is approved in a scientific sense by TUBITAK.
Funders | Funder number |
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Türkiye Bilimsel ve Teknolojik Araştırma Kurumu | 118C353 |
Keywords
- Cardiac MRI
- Chest x-rays
- Medical image quality assessment
- Visual transformers