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

Hongyang Jiang, Mengdi Gao, Heng Li, Richu Jin, Hanpei Miao, Jiang Liu

Research output: Journal PublicationArticlepeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)17-28
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Issue number1
Publication statusPublished - 1 Jan 2023
Externally publishedYes


  • few-shot learning
  • Medical image
  • meta-learning
  • metric-learner
  • transfer-learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management


Dive into the research topics of 'Multi-Learner Based Deep Meta-Learning for Few-Shot Medical Image Classification'. Together they form a unique fingerprint.

Cite this