Abstract
Deep Learning models have transformed various domains, including the healthcare sector, particularly biomedical image classification by learning intricate features and enabling accurate diagnostics pertaining to complex diseases. Recent studies have adopted two different approaches to train DL models: training from scratch and transfer learning. Both approaches demand substantial computational time and resources due to the involvement of massive datasets in model training. These computational demands are further increased due to the design-space exploration required for selecting optimal hyperparameters, which typically necessitates several training rounds. With the growing sizes of datasets, exploring solutions to this problem has recently gained the research community's attention. A plausible solution is to select a subset of the dataset for training and hyperparameter search. This subset, referred to as the corset, must be a representative set of the original dataset. A straightforward approach to selecting the coreset could be employing random sampling, albeit at the cost of compromising the representativeness of the original dataset. A critical limitation of random sampling is the bias towards the dominant classes in an imbalanced dataset. Even if the dataset has inter-class balance, this random sampling will not capture intra-class diversity. This study addresses this issue by introducing an intelligent, lightweight mechanism for coreset selection. Specifically, it proposes a method to extract intra-class diversity, forming per-class clusters that are utilized for the final sampling. We demonstrate the efficacy of the proposed methodology by conducting extensive classification experiments on a well-known biomedical imaging dataset. Results demonstrate that the proposed scheme outperforms the random sampling approach on several performance metrics for uniform conditions.
Abstract (translated)
深度学习模型已经变革了包括医疗保健在内的各个领域，尤其是在通过学习复杂特征并使复杂疾病的准确诊断成为可能的生物医学图像分类方面。最近的研究采用了两种不同的方法来训练深度学习（DL）模型：从头开始训练和迁移学习。这两种方法都需要大量的计算时间和资源，因为大规模数据集在模型训练中起着关键作用。由于需要进行设计空间探索以选择最优超参数，通常需要多次训练轮次，这进一步增加了计算需求。随着数据集规模的增加，解决这一问题的方法越来越受到研究社区的关注。一种可能的解决方案是从整个数据集中选取一个子集用于训练和超参数搜索。这个子集被称为“核心集”（coreset），必须代表原始数据集。简单地通过随机抽样来选择核心集的做法虽然可以实现，但可能会牺牲原始数据集的代表性。随机采样的一个重要限制是对不平衡数据集中主导类别的偏见。即使在类别间平衡的数据集中，随机采样也无法捕捉到类内的多样性。这项研究通过引入一种智能且轻量级的核心集选取机制来解决这一问题。具体而言，它提出了一种提取类内多样性的方法，形成用于最终采样的每类聚类。我们通过对一个著名的生物医学成像数据集进行广泛的分类实验，展示了所提方法的有效性。结果表明，在统一条件下，该方案在多个性能指标上优于随机抽样方法。
URL
https://arxiv.org/abs/2509.21380
