驯服尾部：利用非对称损失与Pade近似克服医学图像长尾类别不平衡问题
Taming the Tail: Leveraging Asymmetric Loss and Pade Approximation to Overcome Medical Image Long-Tailed Class Imbalance
AI总结：
针对医学图像长尾类别不平衡问题，提出一种结合非对称采样技术、基于Pade近似的新型多项式损失函数，在多类医学数据集上验证有效且代码已开源。
摘要：
医疗领域的长尾问题源于不同病症的患病率与数据表征差异导致的数据不平衡，因此需要精准可靠的分类方法。传统损失函数（如交叉熵、二元交叉熵）往往存在不足，无法解决医学图像数据集中高表征类别与低表征类别之间的不平衡问题。我们提出了一种基于Pade近似的新型多项式损失函数，专门用于应对长尾分类的相关挑战。该方法结合了非对称采样技术，以更好地对表征不足的类别进行分类。我们在三个公开医学数据集和一个私有医学数据集上开展了广泛评估。所提损失函数的实现代码已在公开仓库开源：https://github.com/ipankhi/ALPA。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
