Few-Shot Class-Incremental Learning for Retinal Disease Recognition
Abstract
Few-Shot Class-Incremental Learning (FSCIL) techniques are essential for developing Deep Learning (DL) models that can continuously learn new classes with limited samples while retaining existing knowledge. This capability is particularly crucial for DL-based retinal disease diagnosis system, where acquiring large annotated datasets is challenging, and disease phenotypes evolve over time. This paper introduces Re-FSCIL, a novel framework for Few-Shot Class-Incremental Retinal Disease Recognition (FSCIRDR). Re-FSCIL integrates the RETFound model with a fine-grained module, employing a forward-compatible training strategy to improve adaptability, supervised contrastive learning to enhance feature discrimination, and feature fusion for robust representation quality. We convert existing datasets into the FSCIL format and reproduce numerous representative FSCIL methods to create two new benchmarks, RFMiD38 and JSIEC39, specifically for FSCIRDR. Our experimental results demonstrate that Re-FSCIL achieves State-of-the-art (SOTA) performance, significantly surpassing existing FSCIL methods on these benchmarks.
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Zhang et al. leveraged the RETFound foundation model to enhance feature learning in few-shot class-incremental retinal disease recognition.
“Zhang et al. [117] leveraged the RETFound foundation model to enhance feature learning in few-shot class-incremental retinal disease recognition.”
From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [117]
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- Authors
- Zhang, J., Zhao, P., Zhao, Y., Li, C., & Hu, D.
- Affiliations
- National University of Defense Technology (CN)
- Imperial College London (GB)
- Northeastern University (CN)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2025-12
- Volume
- 29
- Issue
- 12
- Pages
- 9001-9012
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W4402593546
- Cited by
- 23
- References
- 45
- Citations by year
- 2026: 11 · 2025: 6 · 2024: 6
- FWCI
- 5.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (62306323)
- China Scholarship Council (202206110005)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- few-shot class-incremental learning, retinal image classification, RETFound, supervised contrastive learning, feature fusion, benchmark dataset, continuous learning, medical image diagnosis
- MeSH terms
- Deep Learning, Humans, Image Interpretation, Computer-Assisted, Retina, Retinal Diseases, Databases, Factual
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