mlsanityData to 14 Oct 2026

Few-shot Class-incremental Learning for Cross-domain Disease Classification

arXiv 2304.05734 ↗First posted 12 Apr 2023cs.CV cs.LGcited by 4

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Abstract (arXiv)

The ability to incrementally learn new classes from limited samples is crucial to the development of artificial intelligence systems for real clinical application. Although existing incremental learning techniques have attempted to address this issue, they still struggle with only few labeled data, particularly when the samples are from varied domains. In this paper, we explore the cross-domain few-shot incremental learning (CDFSCIL) problem. CDFSCIL requires models to learn new classes from very few labeled samples incrementally, and the new classes may be vastly different from the target space. To counteract this difficulty, we propose a cross-domain enhancement constraint and cross-domain data augmentation method. Experiments on MedMNIST show that the classification performance of this method is better than other similar incremental learning methods.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 23 of its 30 references that are in the map. First posted on arXiv 12 Apr 2023.

Builds on

23 of 30 references.

References · 23

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11 more
  1. 2020MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysiscited by 206
  2. 2021Few-Shot Incremental Learning with Continually Evolved Classifierscited by 187
  3. 2021Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learningcited by 96
  4. 2022Constrained Few-shot Class-incremental Learningcited by 94
  5. 2022Few-Shot Class-Incremental Learning by Sampling Multi-Phase Taskscited by 88
  6. 2022Few-Shot Class-Incremental Learning from an Open-Set Perspectivecited by 84
  7. 2020MgSvF: Multi-Grained Slow vs. Fast Framework for Few-Shot Class-Incremental Learningcited by 62
  8. 2020Continual Learning for Domain Adaptation in Chest X-ray Classificationcited by 31
  9. 2022LifeLonger: A Benchmark for Continual Disease Classificationcited by 28
  10. 2021Kernel Continual Learningcited by 27
  11. 2021Adversarial Continual Learning for Multi-Domain Hippocampal Segmentationcited by 14

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2023Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospectscited by 22
  2. 2024Continual Domain Incremental Learning for Privacy-aware Digital Pathologycited by 5
  3. 2025Latest Advancements Towards Catastrophic Forgetting under Data Scarcity: A Comprehensive Survey on Few-Shot Class Incremental Learningcited by 4
  4. 2024CCSI: Continual Class-Specific Impression for Data-free Class Incremental Learningcited by 2