Few-shot Diagnosis of Chest x-rays Using an Ensemble of Random
  Discriminative Subspaces
Kshitiz Garvit Garg Angshuman Paul
Due to the scarcity of annotated data in the medical domain, few-shot
learning may be useful for medical image analysis tasks. We design a few-shot
learning method using an ensemble of random subspaces for the diagnosis of
chest x-rays (CXRs). Our design is computationally efficient and almost 1.8
times faster than method that uses the popular truncated singular value
decomposition (t-SVD) for subspace decomposition. The proposed method is
trained by minimizing a novel loss function that helps create well-separated
clusters of training data in discriminative subspaces. As a result, minimizing
the loss maximizes the distance between the subspaces, making them
discriminative and assisting in better classification. Experiments on
large-scale publicly available CXR datasets yield promising results. Code for
the project will be available at
https://github.com/Few-shot-Learning-on-chest-x-ray/fsl_subspace.