Meta Ordinal Regression Forest for Medical Image Classification With Ordinal Labels

doi:10.1109/JAS.2022.105668

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	Meta Ordinal Regression Forest for Medical Image Classification With Ordinal Labels
	Yiming Lei; Haiping Zhu; Junping Zhang; Hongming Shan
刊名	IEEE/CAA Journal of Automatica Sinica
	2022
卷号	9 期号:7 页码:1233-1247
关键词	Convolutional neural network (CNNs) medical image classification meta-learning ordinal regression random forest
ISSN号	2329-9266
DOI	10.1109/JAS.2022.105668
英文摘要	The performance of medical image classification has been enhanced by deep convolutional neural networks (CNNs), which are typically trained with cross-entropy (CE) loss. However, when the label presents an intrinsic ordinal property in nature, e.g., the development from benign to malignant tumor, CE loss cannot take into account such ordinal information to allow for better generalization. To improve model generalization with ordinal information, we propose a novel meta ordinal regression forest (MORF) method for medical image classification with ordinal labels, which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning framework. The merits of the proposed MORF come from the following two components: A tree-wise weighting net (TWW-Net) and a grouped feature selection (GFS) module. First, the TWW-Net assigns each tree in the forest with a specific weight that is mapped from the classification loss of the corresponding tree. Hence, all the trees possess varying weights, which is helpful for alleviating the tree-wise prediction variance. Second, the GFS module enables a dynamic forest rather than a fixed one that was previously used, allowing for random feature perturbation. During training, we alternatively optimize the parameters of the CNN backbone and TWW-Net in the meta-learning framework through calculating the Hessian matrix. Experimental results on two medical image classification datasets with ordinal labels, i.e., LIDC-IDRI and Breast Ultrasound datasets, demonstrate the superior performances of our MORF method over existing state-of-the-art methods.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48900]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Yiming Lei,Haiping Zhu,Junping Zhang,et al. Meta Ordinal Regression Forest for Medical Image Classification With Ordinal Labels[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(7):1233-1247.
APA	Yiming Lei,Haiping Zhu,Junping Zhang,&Hongming Shan.(2022).Meta Ordinal Regression Forest for Medical Image Classification With Ordinal Labels.IEEE/CAA Journal of Automatica Sinica,9(7),1233-1247.
MLA	Yiming Lei,et al."Meta Ordinal Regression Forest for Medical Image Classification With Ordinal Labels".IEEE/CAA Journal of Automatica Sinica 9.7(2022):1233-1247.