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

Abstract : 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
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Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Monday, August 16, 2021 - 11:54:14 AM
Last modification on : Friday, August 27, 2021 - 3:14:07 PM





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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