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

页码：318 / 326

页数：9

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