基于最优传输特征选择的医学图像分割迁移学习

doi:10.13229/j.cnki.jdxbgxb20210652

摘要/Abstract

摘要：

在无监督领域自适应迁移学习过程中，域无关特征导致模型分割性能下降，而目前并没有针对迁移学习分割模型有效的特征选择方法。为解决该问题，提出了一个基于最优传输的迁移学习通用特征选择模块，可以应用到多种无监督领域自适应图像分割模型中。该模块利用分割准确性加权最优传输选择两个域的最优样本子集，再将样本子集特征进行熵正则化最优传输，得到两个域特征相似性降序列表来去掉域无关特征。将通用特征选择模块应用到三种无监督领域自适应模型中解决新冠肺炎图像分割问题，均在一定程度上提升了模型性能。

关键词: 人工智能, 迁移学习, 无监督领域自适应, 最优传输, 特征选择, 图像分割

Abstract:

In the unsupervised domain adaptive transfer learning process， domain-independent features lead to the degradation of model segmentation performance， but there is no effective feature selection method for transfer learning segmentation model at present. To solve this problem， a general feature selection module for transfer learning was proposed based on optimal transport， which can be applied to various unsupervised domain adaptive image segmentation models. In this module， the optimal sample subsets of two domains are selected by weighted optimal transport of segmentation accuracy， and then the features of sample subsets are subjected to entropy regularized optimal transport， so as to obtain a descending list of similarity between two domains to remove domain-independent features. The universal feature selection module is applied to three unsupervised domain adaptive models to solve the problem of Covid-19 image segmentation， which improves the model performance to a certain extent.

Key words: artificial intelligence, transfer learning, unsupervised domain adaptation, optimal transport, feature selection, image segmentation

中图分类号:

TP18

王生生,姜林延,杨永波. 基于最优传输特征选择的医学图像分割迁移学习[J]. 吉林大学学报(工学版), 2022, 52(7): 1626-1638.

Sheng-sheng WANG,Lin-yan JIANG,Yong-bo YANG. Transfer learning of medical image segmentation based on optimal transport feature selection[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1626-1638.

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特征维度	评价指标	分割准确性加权OT	随机样本选择	OT1	OT2
512	Dice/%	67.3	54.9	64.1	65.6
	Sen/%	71.9	71.1	73.1	71.5
	Pre/%	65.7	46.0	66.2	65.1
2048	Dice/%	70.9	64.2	67.1	68.4
	Sen/%	74.8	72.3	71.2	73.1
	Pre/%	68.6	64.9	65.9	66.5
4096	Dice/%	69.1	69.1	69.1	69.1
	Sen/%	72.7	72.7	72.7	72.7
	Pre/%	65.9	65.9	65.9	65.9

域适应对	评价指标	未进行特征选择	本文方法	升序特征选择	随机特征选择
域适应对	评价指标	4096	↓512	↑512	→512
A→B	Dice/%	71.5	72.2	40.1	60.8
	Sen/%	73.9	75.6	61.6	72.2
	Pre/%	71.6	72.8	32.6	52.1
B→A	Dice/%	63.3	65.8	36.1	53.6
	Sen/%	72.5	71.3	60.3	70.1
	Pre/%	65.3	65.1	29.6	44.9

方法	评价指标	不同特征维度
方法	评价指标	↓512	↓1024	↓2048	3072
Baseline	Dice/%	67.1	69.2	70.5	71.2
	Sen/%	71.6	72.1	74.9	75.8
	Pre/%	65.2	65.3	68.2	69.3
seg‐jdot	Dice/%	74.9	74.8	76.9	78.1
	Sen/%	80.3	79.8	80.6	81.1
	Pre/%	73.1	75.3	75.3	76.8
e‐UDA	Dice/%	74.6	76.7	78.3	78.1
	Sen/%	78.1	80.1	79.9	79.6
	Pre/%	75.3	74.9	76.8	76.1
self‐ensembling	Dice/%	75.4	76.3	77.2	77.9
	Sen/%	79.2	79.4	79.1	80.2
	Pre/%	63.7	64.1	65.3	66.8

方法	评价指标	使用通用特征选择模块前	使用通用特征选择模块后
Baseline	Dice/%	68.9	71.2
	Sen/%	72.8	75.8
	Pre/%	65.3	69.3
seg‐jdot	Dice/%	75.1	78.1
	Sen/%	80.1	81.1
	Pre/%	74.8	76.8
e‐UDA	Dice/%	76.3	78.3
	Sen/%	79.5	79.9
	Pre/%	75.1	76.8
self‐ensembling	Dice/%	76.7	77.9
	Sen/%	79.4	80.2
	Pre/%	64.1	66.8