Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning

Gökay Sezen; İlkay Öksüz

doi:10.1007/978-3-031-16919-9_7

Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning

Gökay Sezen^*, İlkay Öksüz

^*Bu çalışma için yazışmadan sorumlu yazar

Bilgisayar Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

2 Atıf (Scopus)

Özet

Magnetic Resonance Images (MRI) examinations are widely used for diagnosing injuries in the knee. Automatic interpretable detection of meniscus, Anterior Cruciate Ligament (ACL) tears, and general abnormalities from knee MRI is an essential task for automating the clinical diagnosis of knee MRI. This paper proposes a combination of convolution neural network and sequential network deep learning models for detecting general anomalies, ACL tears, and meniscal tears on knee MRI. We combine information from multiple MRI views with transformer blocks for final diagnosis. Also, we did an ablation study which is training with only CNN, and saw the impact of the transformer blocks on the learning. On average, we achieve a performance of 0.905 AUC for three injury cases on MRNet data.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Predictive Intelligence in Medicine - 5th International Workshop, PRIME 2022, Held in Conjunction with MICCAI 2022, Proceedings
Editörler	Islem Rekik, Ehsan Adeli, Sang Hyun Park, Celia Cintas
Yayınlayan	Springer Science and Business Media Deutschland GmbH
Sayfalar	71-78
Sayfa sayısı	8
ISBN (Basılı)	9783031169182
DOI'lar	https://doi.org/10.1007/978-3-031-16919-9_7
Yayın durumu	Yayınlandı - 2022
Etkinlik	5th International Workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Virtual, Online Süre: 22 Eyl 2022 → 22 Eyl 2022

Yayın serisi

Adı	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Hacim	13564 LNCS
ISSN (Basılı)	0302-9743
ISSN (Elektronik)	1611-3349

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

???event.eventtypes.event.conference???	5th International Workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Şehir	Virtual, Online
Periyot	22/09/22 → 22/09/22

Bibliyografik not

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Belgeye Erişim

10.1007/978-3-031-16919-9_7

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Sezen, G., & Öksüz, İ. (2022). Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning. In I. Rekik, E. Adeli, S. H. Park, & C. Cintas (Eds.), Predictive Intelligence in Medicine - 5th International Workshop, PRIME 2022, Held in Conjunction with MICCAI 2022, Proceedings (pp. 71-78). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Hac. 13564 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16919-9_7

Sezen, Gökay ; Öksüz, İlkay. / Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning. Predictive Intelligence in Medicine - 5th International Workshop, PRIME 2022, Held in Conjunction with MICCAI 2022, Proceedings. editör / Islem Rekik ; Ehsan Adeli ; Sang Hyun Park ; Celia Cintas. Springer Science and Business Media Deutschland GmbH, 2022. pp. 71-78 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3706ac67d856400bacdf0ef55259bc6e,

title = "Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning",

abstract = "Magnetic Resonance Images (MRI) examinations are widely used for diagnosing injuries in the knee. Automatic interpretable detection of meniscus, Anterior Cruciate Ligament (ACL) tears, and general abnormalities from knee MRI is an essential task for automating the clinical diagnosis of knee MRI. This paper proposes a combination of convolution neural network and sequential network deep learning models for detecting general anomalies, ACL tears, and meniscal tears on knee MRI. We combine information from multiple MRI views with transformer blocks for final diagnosis. Also, we did an ablation study which is training with only CNN, and saw the impact of the transformer blocks on the learning. On average, we achieve a performance of 0.905 AUC for three injury cases on MRNet data.",

keywords = "Abnormal, ACL, Knee MRI, Meniscus, MRNet",

author = "G{\"o}kay Sezen and İlkay {\"O}ks{\"u}z",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 5th International Workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 ; Conference date: 22-09-2022 Through 22-09-2022",

year = "2022",

doi = "10.1007/978-3-031-16919-9_7",

language = "English",

isbn = "9783031169182",

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 = "71--78",

editor = "Islem Rekik and Ehsan Adeli and Park, {Sang Hyun} and Celia Cintas",

booktitle = "Predictive Intelligence in Medicine - 5th International Workshop, PRIME 2022, Held in Conjunction with MICCAI 2022, Proceedings",

address = "Germany",

}

Sezen, G & Öksüz, İ 2022, Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning. in I Rekik, E Adeli, SH Park & C Cintas (eds), Predictive Intelligence in Medicine - 5th International Workshop, PRIME 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. 13564 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 71-78, 5th International Workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Virtual, Online, 22/09/22. https://doi.org/10.1007/978-3-031-16919-9_7

Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning. / Sezen, Gökay; Öksüz, İlkay.
Predictive Intelligence in Medicine - 5th International Workshop, PRIME 2022, Held in Conjunction with MICCAI 2022, Proceedings. ed. / Islem Rekik; Ehsan Adeli; Sang Hyun Park; Celia Cintas. Springer Science and Business Media Deutschland GmbH, 2022. p. 71-78 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Hac. 13564 LNCS).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

TY - GEN

T1 - Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning

AU - Sezen, Gökay

AU - Öksüz, İlkay

PY - 2022

Y1 - 2022

N2 - Magnetic Resonance Images (MRI) examinations are widely used for diagnosing injuries in the knee. Automatic interpretable detection of meniscus, Anterior Cruciate Ligament (ACL) tears, and general abnormalities from knee MRI is an essential task for automating the clinical diagnosis of knee MRI. This paper proposes a combination of convolution neural network and sequential network deep learning models for detecting general anomalies, ACL tears, and meniscal tears on knee MRI. We combine information from multiple MRI views with transformer blocks for final diagnosis. Also, we did an ablation study which is training with only CNN, and saw the impact of the transformer blocks on the learning. On average, we achieve a performance of 0.905 AUC for three injury cases on MRNet data.

AB - Magnetic Resonance Images (MRI) examinations are widely used for diagnosing injuries in the knee. Automatic interpretable detection of meniscus, Anterior Cruciate Ligament (ACL) tears, and general abnormalities from knee MRI is an essential task for automating the clinical diagnosis of knee MRI. This paper proposes a combination of convolution neural network and sequential network deep learning models for detecting general anomalies, ACL tears, and meniscal tears on knee MRI. We combine information from multiple MRI views with transformer blocks for final diagnosis. Also, we did an ablation study which is training with only CNN, and saw the impact of the transformer blocks on the learning. On average, we achieve a performance of 0.905 AUC for three injury cases on MRNet data.

KW - Abnormal

KW - ACL

KW - Knee MRI

KW - Meniscus

KW - MRNet

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DO - 10.1007/978-3-031-16919-9_7

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AN - SCOPUS:85140435009

SN - 9783031169182

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

SP - 71

EP - 78

BT - Predictive Intelligence in Medicine - 5th International Workshop, PRIME 2022, Held in Conjunction with MICCAI 2022, Proceedings

A2 - Rekik, Islem

A2 - Adeli, Ehsan

A2 - Park, Sang Hyun

A2 - Cintas, Celia

PB - Springer Science and Business Media Deutschland GmbH

T2 - 5th International Workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022

Y2 - 22 September 2022 through 22 September 2022

ER -

Sezen G, Öksüz İ. Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning. In Rekik I, Adeli E, Park SH, Cintas C, editörler, Predictive Intelligence in Medicine - 5th International Workshop, PRIME 2022, Held in Conjunction with MICCAI 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 71-78. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-16919-9_7

Diagnosing Knee Injuries from MRI with Transformer Based Deep Learning

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