Zhang Jinghua, Zhao Peng, Zhao Yongkun, Li Chen, Hu Dewen
IEEE J Biomed Health Inform. 2024 Sep 18;PP. doi: 10.1109/JBHI.2024.3457915.
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.
少样本类别增量学习（FSCIL）技术对于开发深度学习（DL）模型至关重要，这些模型能够在有限样本的情况下持续学习新类别，同时保留现有知识。这种能力对于基于深度学习的视网膜疾病诊断系统尤为关键，因为获取大量带注释的数据集具有挑战性，而且疾病表型会随时间演变。本文介绍了Re-FSCIL，这是一种用于少样本类别增量视网膜疾病识别（FSCIRDR）的新型框架。Re-FSCIL将RETFound模型与一个细粒度模块集成在一起，采用前向兼容训练策略来提高适应性，使用监督对比学习来增强特征辨别能力，并通过特征融合来获得强大的表示质量。我们将现有数据集转换为FSCIL格式，并重现了许多具有代表性的FSCIL方法，以创建两个专门用于FSCIRDR的新基准，即RFMiD38和JSIEC39。我们的实验结果表明，Re-FSCIL实现了当前最优（SOTA）性能，在这些基准上显著超越了现有的FSCIL方法。
