Few-shot Diagnosis of Chest x-rays Using an Ensemble of Random Discriminative Subspaces

Kshitiz; Garvit Garg; Angshuman Paul

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arXiv · 2309.00081

Few-shot Diagnosis of Chest x-rays Using an Ensemble of Random Discriminative Subspaces

Kshitiz·Garvit Garg·Angshuman Paul

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Abstract

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.

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Kshitiz, Garvit Garg, Angshuman Paul. 2023-08-31. Few-shot Diagnosis of Chest x-rays Using an Ensemble of Random Discriminative Subspaces. https://arxiv.org/abs/2309.00081

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