@inproceedings{
kim2023adversarial,
title={Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance},
author={June-Woo Kim and Chihyeon Yoon and Miika Toikkanen and Sangmin Bae and Ho-Young Jung},
booktitle={Deep Generative Models for Health Workshop NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=z1AVG5LDQ7}
}
Deep generative models have emerged as a promising approach in the medical image domain to address data scarcity. However, their use for sequential data like respiratory sounds is less explored. In this work, we propose a straightforward approach to augment imbalanced respiratory sound data using an audio diffusion model as a conditional neural vocoder. We also demonstrate a simple yet effective adversarial fine-tuning method to align features between the synthetic and real respiratory sound samples to improve respiratory sound classification performance. Our experimental results on the ICBHI dataset demonstrate that the proposed adversarial fine-tuning is effective, while only using the conventional augmentation method shows performance degradation. Moreover, our method outperforms the baseline by 2.24% on the ICBHI Score and improves the accuracy of the minority classes up to 26.58%. For the supplementary material, we provide the code at https://github.com/kaen2891/adversarial_fine-tuning_using_generated_respiratory_sound.
Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance
June-Woo Kim Chihyeon Yoon Miika Toikkanen Sangmin Bae Ho-Young Jung