README.md

metadata

license: mit
datasets:
  - alkzar90/NIH-Chest-X-ray-dataset
language:
  - en
metrics:
  - f1
  - accuracy
pipeline_tag: image-classification
tags:
  - Few-Shot Learning
  - medical
  - Computer Vision
  - Image Classification

Few-shot Learning Using Random Subspace

Overview

This repository contains the code for our work on few-shot learning for chest X-ray images. Our approach is detailed in our paper, which can be accessed here.

For a quick overview of our project, visit our website.

Project Summary

Our project presents a novel method for few-shot learning, specifically tailored for the analysis of chest X-ray (CXR) images. The key features of our method include:

• Efficiency: Our approach is nearly 1.8 times faster than the traditional t-SVD method for subspace decomposition.
• Effective Clustering: The method ensures the creation of well-separated clusters of training data in discriminative subspaces.
• Promising Results: We have tested our method on large-scale CXR datasets, yielding encouraging outcomes.

Contact

Reach out to the authors [details provided in the project page]