TY - GEN
T1 - A Self-ensembling Framework for Semi-supervised Knee Cartilage Defects Assessment with Dual-Consistency
AU - Huo, Jiayu
AU - Si, Liping
AU - Ouyang, Xi
AU - Xuan, Kai
AU - Yao, Weiwu
AU - Xue, Zhong
AU - Wang, Qian
AU - Shen, Dinggang
AU - Zhang, Lichi
N1 - Funding Information:
Acknowledgement. This work was supported by the National Key Research and Development Program of China (2018YFC0116400), STCSM (19QC1400600, 17411953300, 18JC1420305), Shanghai Pujiang Program (19PJ1406800), and Interdisciplinary Program of Shanghai Jiao Tong University.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Knee osteoarthritis (OA) is one of the most common musculoskeletal disorders and requires early-stage diagnosis. Nowadays, the deep convolutional neural networks have achieved greatly in the computer-aided diagnosis field. However, the construction of the deep learning models usually requires great amounts of annotated data, which is generally high-cost. In this paper, we propose a novel approach for knee cartilage defects assessment, including severity classification and lesion localization. This can be treated as a subtask of knee OA diagnosis. Particularly, we design a self-ensembling framework, which is composed of a student network and a teacher network with the same structure. The student network learns from both labeled data and unlabeled data and the teacher network averages the student model weights through the training course. A novel attention loss function is developed to obtain accurate attention masks. With dual-consistency checking of the attention in the lesion classification and localization, the two networks can gradually optimize the attention distribution and improve the performance of each other, whereas the training relies on partially labeled data only and follows the semi-supervised manner. Experiments show that the proposed method can significantly improve the self-ensembling performance in both knee cartilage defects classification and localization, and also greatly reduce the needs of annotated data.
AB - Knee osteoarthritis (OA) is one of the most common musculoskeletal disorders and requires early-stage diagnosis. Nowadays, the deep convolutional neural networks have achieved greatly in the computer-aided diagnosis field. However, the construction of the deep learning models usually requires great amounts of annotated data, which is generally high-cost. In this paper, we propose a novel approach for knee cartilage defects assessment, including severity classification and lesion localization. This can be treated as a subtask of knee OA diagnosis. Particularly, we design a self-ensembling framework, which is composed of a student network and a teacher network with the same structure. The student network learns from both labeled data and unlabeled data and the teacher network averages the student model weights through the training course. A novel attention loss function is developed to obtain accurate attention masks. With dual-consistency checking of the attention in the lesion classification and localization, the two networks can gradually optimize the attention distribution and improve the performance of each other, whereas the training relies on partially labeled data only and follows the semi-supervised manner. Experiments show that the proposed method can significantly improve the self-ensembling performance in both knee cartilage defects classification and localization, and also greatly reduce the needs of annotated data.
KW - Knee osteoarthritis
KW - Self-ensembling model
KW - Semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85092900250&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59354-4_19
DO - 10.1007/978-3-030-59354-4_19
M3 - Conference contribution
AN - SCOPUS:85092900250
SN - 9783030593537
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 200
EP - 209
BT - Predictive Intelligence in Medicine - 3rd International Workshop, PRIME 2020, Held in Conjunction with MICCAI 2020, Proceedings
A2 - Rekik, Islem
A2 - Adeli, Ehsan
A2 - Park, Sang Hyun
A2 - Valdés Hernández, Maria del C.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Workshop on Predictive Intelligence in Medicine, PRIME 2020, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
Y2 - 8 October 2020 through 8 October 2020
ER -