Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging

arXiv:2404.02656v1 Announce Type: new
Abstract: Unlike typical visual scene recognition domains, in which massive datasets are accessible to deep neural networks, medical image interpretations are often obstructed by the paucity of data. In this paper, we investigate the effectiveness of data-based few-shot learning in medical imaging by exploring different data attribute representations in a low-dimensional space. We introduce different types of non-negative matrix factorization (NMF) in few-shot learning, addressing the data scarcity issue in medical image classification. Extensive empirical studies …

abstract arxiv cs.ai cs.cv data datasets domains feature few-shot few-shot learning image imaging massive medical medical imaging negative networks neural networks paper recognition representation type visual