/LRH-Net

Official Implementation of LRH-Net: A Multi-Level Knowledge Distillation Approach for Low-Resource Heart Network

Primary LanguageJupyter Notebook

LRH-Net: A Multi-Level Knowledge Distillation Approach for Low-Resource Heart Network

Pytorch implementation for the LRH-Net: A Multi-Level Knowledge Distillation Approach for Low-Resource Heart Network (FAIR, MICCAI 2022).

Proposed Student Architecture

Optimized Neural Network used

Proposed Knowledge Distillation methods for ECG classification

Parallel-MLKD Sequential-MLKD

🔗 Links

[Arxiv] [Paper]

Installation

Install anaconda/miniconda
Required packages

  $ conda env create --name lrhnet --file env.yml
  $ conda activate lrhnet

Install PyTorch

Datasets

Download Datasets from https://moody-challenge.physionet.org/2020 and https://moody-challenge.physionet.org/2021

🚀 Contributions by LRH-Net

Main contributions of this paper are:

  1. A real-time cardiovascular disease detection model which is 106x smaller than a large-scale model and 12x times smaller than the existing low-scale model.
  2. A Multi-Level knowledge distillation approach to improve the performance of LRH-Net (student model) and to reduce the number of electrodes and input leads data required for the student model .
  3. Performed evaluation on a very diverse, publicly available and combination of multiple datasets to increase its desirability.

Results

Futher Reseach can focus on:

Further research can focus on lowering the performance gap in low-lead configurations by optimizing the number of steps required to distill majority of the critical information by varying the levels of MLKD so that the classification performance on hard-to-classify diseases does not get severely affected.

Citation

If you use the code or results in your research, please use the following BibTeX entry.

@InProceedings{10.1007/978-3-031-18523-6_18,
author="Chauhan, Ekansh
and Guptha, Swathi
and Reddy, Likith
and Raju, Bapi",
title="LRH-Net: A Multi-level Knowledge Distillation Approach for Low-Resource Heart Network",
booktitle="Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health",
year="2022",
publisher="Springer Nature Switzerland",
address="Cham",
pages="190--201",
isbn="978-3-031-18523-6"
}