A Pseudo-labeling Approach to Semi-supervised Organ Segmentation

被引：2
|
作者：
Gao, Jianwei [1 ]
Xu, Juan [1 ]
Fei, Honggao [1 ]
机构：
[1] Digital Hlth China Technol Co LTD, Beijing, Peoples R China
来源：
FAST AND LOW-RESOURCE SEMI-SUPERVISED ABDOMINAL ORGAN SEGMENTATION, FLARE 2022 | 2022年 / 13816卷
关键词：
Abdominal organ segmentation; Semi-supervised; Pseudo-labeling;
D O I：
10.1007/978-3-031-23911-3_28
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we adopt a "pseudo-labeling" approach to semi-supervised learning based on 50 labeled images and 2000 unlabeled images. This approach yields a model with 0.7496 mean DSC on the validation set, outperforming the 0.6903 mean DSC of the model with only 50 labeled images.
引用
收藏
页码：318 / 326
页数：9
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