用自适应难易度权重提升小样本医学图像诊断效果
FOSSIL: Regret-Minimizing Curriculum Learning for Metadata-Free and Low-Data Mpox Diagnosis
- 根据样本不确定性动态调整训练重点，构建四阶段渐进式学习流程
- 在无元数据小样本下实现0.9573的AUC与0.053的ECE校准误差
- 适合数据稀缺场景下的医疗影像模型训练，兼具高效性与可解释性
小样本且不平衡的生物医学数据集中的深度学习仍受限于优化不稳定和泛化能力差。本文首次将FOSSIL（基于样本敏感重要性学习的灵活优化）应用于生物医学领域，提出一种最小化后悔值的加权框架，依据样本难度自适应调整训练侧重。采用softmax不确定性作为连续难度度量，构建从简单到极难的四阶段课程学习策略，并集成至卷积与Transformer架构中，用于猴痘皮损诊断。在所有设置下，FOSSIL显著提升判别能力（AUC = 0.9573）、校准性能（ECE = 0.053），并在真实世界扰动下保持鲁棒性，优于传统基线，无需元数据、人工标注或合成增强。结果表明，FOSSIL是一种通用、数据高效且可解释的困难感知学习框架，适用于数据稀缺条件下的医学影像分析。
原文摘要 · Abstract (English)
Deep learning in small and imbalanced biomedical datasets remains fundamentally constrained by unstable optimization and poor generalization. We present the first biomedical implementation of FOSSIL (Flexible Optimization via Sample-Sensitive Importance Learning), a regret-minimizing weighting framework that adaptively balances training emphasis according to sample difficulty. Using softmax-based uncertainty as a continuous measure of difficulty, we construct a four-stage curriculum (Easy-Very Hard) and integrate FOSSIL into both convolutional and transformer-based architectures for Mpox skin lesion diagnosis. Across all settings, FOSSIL substantially improves discrimination (AUC = 0.9573), calibration (ECE = 0.053), and robustness under real-world perturbations, outperforming conventional baselines without metadata, manual curation, or synthetic augmentation. The results position FOSSIL as a generalizable, data-efficient, and interpretable framework for difficulty-aware learning in medical imaging under data scarcity.
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