A Self-ensembling Framework for Semi-supervised Knee Cartilage Defects Assessment with Dual-Consistency

Jiayu Huo, Liping Si, Xi Ouyang, Kai Xuan, Weiwu Yao, Zhong Xue, Qian Wang, Dinggang Shen, Lichi Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationPredictive Intelligence in Medicine - 3rd International Workshop, PRIME 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsIslem Rekik, Ehsan Adeli, Sang Hyun Park, Maria del C. Valdés Hernández
PublisherSpringer Science and Business Media Deutschland GmbH
Pages200-209
Number of pages10
ISBN (Print)9783030593537
DOIs
Publication statusPublished - 2020
Event3rd International Workshop on Predictive Intelligence in Medicine, PRIME 2020, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 2020 Oct 82020 Oct 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12329 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Predictive Intelligence in Medicine, PRIME 2020, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2020
CountryPeru
CityLima
Period20/10/820/10/8

Keywords

  • Knee osteoarthritis
  • Self-ensembling model
  • Semi-supervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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