针对医学图像长尾分布，提出新型不对称损失函数提升罕见病识别准确率。
Taming the Tail: Leveraging Asymmetric Loss and Pade Approximation to Overcome Medical Image Long-Tailed Class Imbalance
- 基于帕德逼近设计多项式损失，缓解类别不平衡问题。
- 在三个公开及一个私有数据集上均显著提升小样本类分类性能。
- 适合医疗影像中罕见病检测场景，尤其关注低频疾病识别。
医疗领域中的长尾问题源于不同疾病在数据中的流行率和表征差异导致的数据不平衡，亟需精确可靠的分类方法。传统交叉熵等损失函数难以应对高频率与低频率类别间的失衡。本文提出一种基于帕德逼近的新型多项式损失函数，专门解决长尾分类挑战，并结合不对称采样技术以更好识别低频类别。我们在三个公开医疗数据集和一个私有数据集上进行了广泛评估。所提损失函数已开源至：https://github.com/ipankhi/ALPA。
原文摘要 · Abstract (English)
Long-tailed problems in healthcare emerge from data imbalance due to variability in the prevalence and representation of different medical conditions, warranting the requirement of precise and dependable classification methods. Traditional loss functions such as cross-entropy and binary cross-entropy are often inadequate due to their inability to address the imbalances between the classes with high representation and the classes with low representation found in medical image datasets. We introduce a novel polynomial loss function based on Pade approximation, designed specifically to overcome the challenges associated with long-tailed classification. This approach incorporates asymmetric sampling techniques to better classify under-represented classes. We conducted extensive evaluations on three publicly available medical datasets and a proprietary medical dataset. Our implementation of the proposed loss function is open-sourced in the public repository:https://github.com/ipankhi/ALPA.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。