---
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](https://openreview.net/pdf?id=AF97JZpgPe).

For a quick overview of our project, visit our [website](https://few-shot-learning-on-chest-x-ray.github.io/Project-Page/).

## 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]