基于置信学习和协同训练的医学图像分割方法

doi:10.13229/j.cnki.jdxbgxb.20230833

Abstract

Abstract:

Confident learning plays an important role in the training of low-quality labeled data of medical images， but the current application of confident learning is based on the mean teacher model， and the possibility on other networks is not discussed. To solve this problem， a segmentation model based on confident learning and collaborative training is proposed in this paper. The model uses two different networks， encourages the output of the two networks to be consistent， and then compares the output of one network with the original low-quality label by using confident learning to modify the low-quality labeled data so as to provide an effective training reference. The proposed model has been compared on three different modal medical image datasets， and the experimental results show that the segmentation effect of the model is better than that of the existing confident learning model.

Key words: computer application, medical image segmentation, confident learning, collaborative training

CLC Number:

TP391

Hong-wei ZHAO,Ming-zhu ZHOU,Ping-ping LIU,Qiu-zhan ZHOU. Medical image segmentation based on confident learning and collaborative training[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(5): 1675-1681.

Figures/Tables 7

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References 12

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k值	Dice	HD95
1：2	0.594 1	27.717 4
1：3	0.615 8	19.785 0
1：4	0.584 5	24.062 4

ω值	Dice	HD95
0	0.600 2	21.504 8
5	0.615 8	19.785 0
10	0.605 3	20.092 9

数据集	方法	Dice	HD95
皮肤病病灶分割	Full Decoupled MTCL	0.582 5 0.843 5 0.854 3	37.885 7 9.567 1 10.076 1
	CTCL	0.868 5	7.956 0
肺部X射线	Full Decoupled MTCL	0.863 9 0.892 9 0.881 3	6.001 2 3.059 5 4.069 2
	CTCL	0.904 9	2.407 3
颅内出血	Full Decoupled MTCL	0.419 9 0.585 2 0.578 1	22.213 0 24.473 3 20.539 0
	CTCL	0.615 8	19.785 0

U-Net	Transformer	协同训练	置信学习	Dice	HD95
√				0.419 9	22.213 0
	√			0.402 9	34.757 6
√	√	√		0.600 2	21.504 8
√	√	√	√	0.615 8	19.785 0

Medical image segmentation based on confident learning and collaborative training

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