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Communication Dans Un Congrès Année : 2020

Few-sample Multi-organ Abdominal Image Segmentation with Mean Teacher Model

Pingyi Chen
  • Fonction : Auteur
Tianyu Chen
  • Fonction : Auteur
Zhiqiang Yang
Tian Wang
  • Fonction : Auteur
Mengyi Zhang
  • Fonction : Auteur

Résumé

Medical segmentation is a significant task since it provides valuable information for diagnosis. In the recent years, convolutional neural networks have achieved great success in this field. However, the number of medical images is often limited which cannot support large networks to be trained. Thus, overfitting is much more common in medical images than other tasks. Also, to get annotated images is very costly and time-consuming. We stimulate an extreme condition where the number of the sample is so limited that we adopt a Mean Teacher Model to avoid overfitting. We build two models - student and teacher, with same structure and alternatively trained. We apply consistency loss to update the parameters of student and use Exponential Moving Average to compute parameters of teacher from student model. All code can be found in https://github.com/cpystan/MT-Model.
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Dates et versions

hal-03320685 , version 1 (16-08-2021)

Identifiants

Citer

Pingyi Chen, Tianyu Chen, Zhiqiang Yang, Tian Wang, Mengyi Zhang, et al.. Few-sample Multi-organ Abdominal Image Segmentation with Mean Teacher Model. 2020 39th Chinese Control Conference (CCC), Jul 2020, Shenyang, China. pp.6613-6617, ⟨10.23919/CCC50068.2020.9189187⟩. ⟨hal-03320685⟩
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