Shifted Windows Transformers for Medical Image Quality Assessment

Caner Özer; Arda Güler; Aysel Türkvatan Cansever; Deniz Alis; Ercan Karaarslan; İlkay Öksüz

doi:10.1007/978-3-031-21014-3_44

Shifted Windows Transformers for Medical Image Quality Assessment

Caner Özer^*, Arda Güler, Aysel Türkvatan Cansever, Deniz Alis, Ercan Karaarslan, İlkay Öksüz

^*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

3 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Machine Learning in Medical Imaging - 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Proceedings
Editörler	Chunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Zhiming Cui
Yayınlayan	Springer Science and Business Media Deutschland GmbH
Sayfalar	425-435
Sayfa sayısı	11
ISBN (Basılı)	9783031210136
DOI'lar	https://doi.org/10.1007/978-3-031-21014-3_44
Yayın durumu	Yayınlandı - 2022
Etkinlik	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 Süre: 18 Eyl 2022 → 18 Eyl 2022

Yayın serisi

Adı	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Hacim	13583 LNCS
ISSN (Basılı)	0302-9743
ISSN (Elektronik)	1611-3349

???event.eventtypes.event.conference???

???event.eventtypes.event.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
Ülke/Bölge	Singapore
Şehir	Singapore
Periyot	18/09/22 → 18/09/22

Bibliyografik not

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Finansman

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.

Finansörler	Finansör numarası
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu	118C353

Belgeye Erişim

10.1007/978-3-031-21014-3_44

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Özer, C., Güler, A., Cansever, A. T., Alis, D., Karaarslan, E., & Öksüz, İ. (2022). Shifted Windows Transformers for Medical Image Quality Assessment. In C. Lian, X. Cao, I. Rekik, X. Xu, & Z. Cui (Eds.), Machine Learning in Medical Imaging - 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Proceedings (pp. 425-435). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Hac. 13583 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-21014-3_44

Özer, Caner ; Güler, Arda ; Cansever, Aysel Türkvatan et al. / Shifted Windows Transformers for Medical Image Quality Assessment. Machine Learning in Medical Imaging - 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Proceedings. editör / Chunfeng Lian ; Xiaohuan Cao ; Islem Rekik ; Xuanang Xu ; Zhiming Cui. Springer Science and Business Media Deutschland GmbH, 2022. pp. 425-435 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{68c58969fa184cbb8b673f07877ea9bc,

title = "Shifted Windows Transformers for Medical Image Quality Assessment",

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.",

keywords = "Cardiac MRI, Chest x-rays, Medical image quality assessment, Visual transformers",

author = "Caner {\"O}zer and Arda G{\"u}ler and Cansever, {Aysel T{\"u}rkvatan} and Deniz Alis and Ercan Karaarslan and İlkay {\"O}ks{\"u}z",

note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 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 ; Conference date: 18-09-2022 Through 18-09-2022",

year = "2022",

doi = "10.1007/978-3-031-21014-3_44",

language = "English",

isbn = "9783031210136",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "425--435",

editor = "Chunfeng Lian and Xiaohuan Cao and Islem Rekik and Xuanang Xu and Zhiming Cui",

booktitle = "Machine Learning in Medical Imaging - 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Proceedings",

address = "Germany",

}

Özer, C, Güler, A, Cansever, AT, Alis, D, Karaarslan, E & Öksüz, İ 2022, Shifted Windows Transformers for Medical Image Quality Assessment. in C Lian, X Cao, I Rekik, X Xu & Z Cui (eds), Machine Learning in Medical Imaging - 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), hac. 13583 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 425-435, 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, 18/09/22. https://doi.org/10.1007/978-3-031-21014-3_44

Shifted Windows Transformers for Medical Image Quality Assessment. / Özer, Caner; Güler, Arda; Cansever, Aysel Türkvatan et al.
Machine Learning in Medical Imaging - 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Proceedings. ed. / Chunfeng Lian; Xiaohuan Cao; Islem Rekik; Xuanang Xu; Zhiming Cui. Springer Science and Business Media Deutschland GmbH, 2022. p. 425-435 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Hac. 13583 LNCS).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Shifted Windows Transformers for Medical Image Quality Assessment

AU - Özer, Caner

AU - Güler, Arda

AU - Cansever, Aysel Türkvatan

AU - Alis, Deniz

AU - Karaarslan, Ercan

AU - Öksüz, İlkay

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Cardiac MRI

KW - Chest x-rays

KW - Medical image quality assessment

KW - Visual transformers

UR - http://www.scopus.com/inward/record.url?scp=85144826362&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-21014-3_44

DO - 10.1007/978-3-031-21014-3_44

M3 - Conference contribution

AN - SCOPUS:85144826362

SN - 9783031210136

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 425

EP - 435

BT - Machine Learning in Medical Imaging - 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Proceedings

A2 - Lian, Chunfeng

A2 - Cao, Xiaohuan

A2 - Rekik, Islem

A2 - Xu, Xuanang

A2 - Cui, Zhiming

PB - Springer Science and Business Media Deutschland GmbH

T2 - 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

Y2 - 18 September 2022 through 18 September 2022

ER -

Özer C, Güler A, Cansever AT, Alis D, Karaarslan E, Öksüz İ. Shifted Windows Transformers for Medical Image Quality Assessment. In Lian C, Cao X, Rekik I, Xu X, Cui Z, editörler, Machine Learning in Medical Imaging - 13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 425-435. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-21014-3_44