  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 are conducted in terms of
validating the effectiveness of NMF, especially its supervised variants (e.g.,
discriminative NMF, and supervised and constrained NMF with sparseness), and
the comparison with principal component analysis (PCA), i.e., the collaborative
representation-based dimensionality reduction technique derived from
eigenvectors. With 14 different datasets covering 11 distinct illness
categories, thorough experimental results and comparison with related
techniques demonstrate that NMF is a competitive alternative to PCA for
few-shot learning in medical imaging, and the supervised NMF algorithms are
more discriminative in the subspace with greater effectiveness. Furthermore, we
show that the part-based representation of NMF, especially its supervised
variants, is dramatically impactful in detecting lesion areas in medical
imaging with limited samples.
