标题： 迭代误分类误差训练（IMET）：一种优化的神经网络图像分类训练技术 显示英文标题

标题： Iterative Misclassification Error Training (IMET): An Optimized Neural Network Training Technique for Image Classification

摘要： 深度学习模型在医学数据集上已被证明对于从图像中进行准确的诊断预测是有效的。 然而，医学数据集通常包含噪声、错误标记或泛化能力差的图像，特别是在边缘案例和异常结果中。 此外，高质量的数据集样本量通常较小，可能导致过拟合，即模型记忆噪声而不是学习可泛化的模式。 这尤其可能在医学诊断中带来严重风险，因为分类错误的风险可能影响人类生命。 几种数据高效的训练策略已经出现以解决这些限制。 特别是，核心集选择识别最具代表性的样本子集，使训练能够近似全数据集性能，同时减少计算开销。 另一方面，课程学习依赖于逐步增加训练难度并加速收敛。 然而，开发一个能够在不同领域、数据集和模型中通用的难度排序机制，同时减少计算任务，仍然是一个挑战。 在本文中，我们引入了迭代误分类误差训练（IMET），这是一种受课程学习和核心集选择启发的新框架。 IMET方法旨在识别误分类样本，以优化训练过程，同时优先关注边缘案例场景和罕见结果。 本文将IMET在基准医学图像分类数据集上与最先进的ResNet架构进行了评估。 论文还展示了IMET在增强医学图像分析中模型鲁棒性和准确性方面的潜力。 显示英文摘要

摘要： Deep learning models have proven to be effective on medical datasets for accurate diagnostic predictions from images. However, medical datasets often contain noisy, mislabeled, or poorly generalizable images, particularly for edge cases and anomalous outcomes. Additionally, high quality datasets are often small in sample size that can result in overfitting, where models memorize noise rather than learn generalizable patterns. This in particular, could pose serious risks in medical diagnostics where the risk associated with mis-classification can impact human life. Several data-efficient training strategies have emerged to address these constraints. In particular, coreset selection identifies compact subsets of the most representative samples, enabling training that approximates full-dataset performance while reducing computational overhead. On the other hand, curriculum learning relies on gradually increasing training difficulty and accelerating convergence. However, developing a generalizable difficulty ranking mechanism that works across diverse domains, datasets, and models while reducing the computational tasks and remains challenging. In this paper, we introduce Iterative Misclassification Error Training (IMET), a novel framework inspired by curriculum learning and coreset selection. The IMET approach is aimed to identify misclassified samples in order to streamline the training process, while prioritizing the model's attention to edge case senarious and rare outcomes. The paper evaluates IMET's performance on benchmark medical image classification datasets against state-of-the-art ResNet architectures. The results demonstrating IMET's potential for enhancing model robustness and accuracy in medical image analysis are also presented in the paper.