Details
| Original language | English |
|---|---|
| Title of host publication | 2023 International Joint Conference on Neural Networks (IJCNN) |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (electronic) | 9781665488679 |
| ISBN (print) | 978-1-6654-8868-6 |
| Publication status | Published - 2023 |
| Event | International Joint Conference on Neural Networks, IJCNN 2023 - Gold Coast, Australia Duration: 18 Jun 2023 → 23 Jun 2023 |
Publication series
| Name | Proceedings of the International Joint Conference on Neural Networks |
|---|---|
| Volume | 2023-June |
| ISSN (Print) | 2161-4393 |
| ISSN (electronic) | 2161-4407 |
Abstract
Analysis of respiratory sounds is an area where deep neural networks (DNNs) may benefit clinicians and patients for diagnostic purposes due to their classification power. However, explaining the predictions made by DNNs remains a challenge. Currently, most explanation methods focus on post-hoc explanations, where a separate explanatory model is used to explain a trained DNN. Due to the complex nature of respiratory sound classification pipeline involving signal processing such as frequency analysis and wavelet analysis, post-hoc methods cannot uncover the underlying inference process of DNNs, highlighting the importance of designing DNNs with intrinsic interpretability. In this paper, we propose a self-explaining DNN for respiratory sound classification based on prototype learning. Our model explains its behavior by generating sample prototypes while attaching these prototypes to a layer inside the neural network. Furthermore, we design a scale-free interpretability mechanism, in which the model reaches its final decision by dissecting the input and looking for similarities between several parts of the input and the prototypes. The experimental findings on the largest public respiratory sound database demonstrate that our method achieves comparable, sometimes better, performance with the non-interpretable counterparts while offering state-of-the-art interpretability. The code will be released upon acceptance.
Keywords
- prototype learning, respiratory sound classification, scale-free interpretability, self-explaining neural networks
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Artificial Intelligence
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2023 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of the International Joint Conference on Neural Networks; Vol. 2023-June).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Self-Explaining Neural Networks for Respiratory Sound Classification with Scale-free Interpretability
AU - Ren, Zhao
AU - Nguyen, Thanh Tam
AU - Zahed, Mohammad Mehdi
AU - Nejdl, Wolfgang
N1 - Funding Information: Acknowledgment. This research was funded by the Federal Ministry of Education and Research (BMBF), Germany under the project LeibnizKILabor (No. 01DD20003), and the research projects “IIP-Ecosphere” granted by the German Federal Ministry for Economics and Climate Action (BMWK) (No. 01MK20006A).
PY - 2023
Y1 - 2023
N2 - Analysis of respiratory sounds is an area where deep neural networks (DNNs) may benefit clinicians and patients for diagnostic purposes due to their classification power. However, explaining the predictions made by DNNs remains a challenge. Currently, most explanation methods focus on post-hoc explanations, where a separate explanatory model is used to explain a trained DNN. Due to the complex nature of respiratory sound classification pipeline involving signal processing such as frequency analysis and wavelet analysis, post-hoc methods cannot uncover the underlying inference process of DNNs, highlighting the importance of designing DNNs with intrinsic interpretability. In this paper, we propose a self-explaining DNN for respiratory sound classification based on prototype learning. Our model explains its behavior by generating sample prototypes while attaching these prototypes to a layer inside the neural network. Furthermore, we design a scale-free interpretability mechanism, in which the model reaches its final decision by dissecting the input and looking for similarities between several parts of the input and the prototypes. The experimental findings on the largest public respiratory sound database demonstrate that our method achieves comparable, sometimes better, performance with the non-interpretable counterparts while offering state-of-the-art interpretability. The code will be released upon acceptance.
AB - Analysis of respiratory sounds is an area where deep neural networks (DNNs) may benefit clinicians and patients for diagnostic purposes due to their classification power. However, explaining the predictions made by DNNs remains a challenge. Currently, most explanation methods focus on post-hoc explanations, where a separate explanatory model is used to explain a trained DNN. Due to the complex nature of respiratory sound classification pipeline involving signal processing such as frequency analysis and wavelet analysis, post-hoc methods cannot uncover the underlying inference process of DNNs, highlighting the importance of designing DNNs with intrinsic interpretability. In this paper, we propose a self-explaining DNN for respiratory sound classification based on prototype learning. Our model explains its behavior by generating sample prototypes while attaching these prototypes to a layer inside the neural network. Furthermore, we design a scale-free interpretability mechanism, in which the model reaches its final decision by dissecting the input and looking for similarities between several parts of the input and the prototypes. The experimental findings on the largest public respiratory sound database demonstrate that our method achieves comparable, sometimes better, performance with the non-interpretable counterparts while offering state-of-the-art interpretability. The code will be released upon acceptance.
KW - prototype learning
KW - respiratory sound classification
KW - scale-free interpretability
KW - self-explaining neural networks
UR - http://www.scopus.com/inward/record.url?scp=85169566636&partnerID=8YFLogxK
U2 - 10.1109/IJCNN54540.2023.10191600
DO - 10.1109/IJCNN54540.2023.10191600
M3 - Conference contribution
AN - SCOPUS:85169566636
SN - 978-1-6654-8868-6
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2023 International Joint Conference on Neural Networks (IJCNN)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Joint Conference on Neural Networks, IJCNN 2023
Y2 - 18 June 2023 through 23 June 2023
ER -