TY - GEN
T1 - Multi-modality Contrastive Learning for Sarcopenia Screening from Hip X-rays and Clinical Information
AU - Jin, Qiangguo
AU - Zou, Changjiang
AU - Cui, Hui
AU - Sun, Changming
AU - Huang, Shu Wei
AU - Kuo, Yi Jie
AU - Xuan, Ping
AU - Cao, Leilei
AU - Su, Ran
AU - Wei, Leyi
AU - Duh, Henry B.L.
AU - Chen, Yu Pin
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023
Y1 - 2023
N2 - Sarcopenia is a condition of age-associated muscle degeneration that shortens the life expectancy in those it affects, compared to individuals with normal muscle strength. Accurate screening for sarcopenia is a key process of clinical diagnosis and therapy. In this work, we propose a novel multi-modality contrastive learning (MM-CL) based method that combines hip X-ray images and clinical parameters for sarcopenia screening. Our method captures the long-range information with Non-local CAM Enhancement, explores the correlations in visual-text features via Visual-text Feature Fusion, and improves the model’s feature representation ability through Auxiliary contrastive representation. Furthermore, we establish a large in-house dataset with 1,176 patients to validate the effectiveness of multi-modality based methods. Significant performances with an AUC of 84.64%, ACC of 79.93%, F1 of 74.88%, SEN of 72.06%, SPC of 86.06%, and PRE of 78.44%, show that our method outperforms other single-modality and multi-modality based methods.
AB - Sarcopenia is a condition of age-associated muscle degeneration that shortens the life expectancy in those it affects, compared to individuals with normal muscle strength. Accurate screening for sarcopenia is a key process of clinical diagnosis and therapy. In this work, we propose a novel multi-modality contrastive learning (MM-CL) based method that combines hip X-ray images and clinical parameters for sarcopenia screening. Our method captures the long-range information with Non-local CAM Enhancement, explores the correlations in visual-text features via Visual-text Feature Fusion, and improves the model’s feature representation ability through Auxiliary contrastive representation. Furthermore, we establish a large in-house dataset with 1,176 patients to validate the effectiveness of multi-modality based methods. Significant performances with an AUC of 84.64%, ACC of 79.93%, F1 of 74.88%, SEN of 72.06%, SPC of 86.06%, and PRE of 78.44%, show that our method outperforms other single-modality and multi-modality based methods.
KW - Contrastive learning
KW - Multi-modality feature fusion
KW - Sarcopenia screening
UR - http://www.scopus.com/inward/record.url?scp=85174743637&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85174743637&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-43987-2_9
DO - 10.1007/978-3-031-43987-2_9
M3 - Conference contribution
AN - SCOPUS:85174743637
SN - 9783031439865
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 85
EP - 94
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
A2 - Greenspan, Hayit
A2 - Greenspan, Hayit
A2 - Madabhushi, Anant
A2 - Mousavi, Parvin
A2 - Salcudean, Septimiu
A2 - Duncan, James
A2 - Syeda-Mahmood, Tanveer
A2 - Taylor, Russell
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Y2 - 8 October 2023 through 12 October 2023
ER -