Multi-Learner Based Deep Meta-Learning for Few-Shot Medical Image Classification

计算机科学 学习迁移 人工智能 机器学习 杠杆(统计) 深度学习 公制(单位) 任务(项目管理) 编码器 一般化 元学习(计算机科学) 上下文图像分类 图像(数学) 数学分析 管理 经济 运营管理 操作系统 数学
作者
Hongyang Jiang,Mengdi Gao,Heng Li,Richu Jin,Hanpei Miao,Jiang Liu
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:27 (1): 17-28 被引量:25
标识
DOI:10.1109/jbhi.2022.3215147
摘要

Few-shot learning (FSL) is promising in the field of medical image analysis due to high cost of establishing high-quality medical datasets. Many FSL approaches have been proposed in natural image scenes. However, present FSL methods are rarely evaluated on medical images and the FSL technology applicable to medical scenarios need to be further developed. Meta-learning has supplied an optional framework to address the challenging FSL setting. In this paper, we propose a novel multi-learner based FSL method for multiple medical image classification tasks, combining meta-learning with transfer-learning and metric-learning. Our designed model is composed of three learners, including auto-encoder, metric-learner and task-learner. In transfer-learning, all the learners are trained on the base classes. In the ensuing meta-learning, we leverage multiple novel tasks to fine-tune the metric-learner and task-learner in order to fast adapt to unseen tasks. Moreover, to further boost the learning efficiency of our model, we devised real-time data augmentation and dynamic Gaussian disturbance soft label (GDSL) scheme as effective generalization strategies of few-shot classification tasks. We have conducted experiments for three-class few-shot classification tasks on three newly-built challenging medical benchmarks, BLOOD, PATH and CHEST. Extensive comparisons to related works validated that our method achieved top performance both on homogeneous medical datasets and cross-domain datasets.
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