Details
| Original language | English |
|---|---|
| Title of host publication | 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing |
| Subtitle of host publication | ICASSP 2022 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 9087-9091 |
| Number of pages | 5 |
| ISBN (electronic) | 9781665405409 |
| Publication status | Published - May 2022 |
| Event | 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore Duration: 23 May 2022 → 27 May 2022 |
Publication series
| Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
|---|---|
| Volume | 2022-May |
| ISSN (Print) | 1520-6149 |
Abstract
Remote screening of respiratory diseases has been widely studied as a non-invasive and early instrument for diagnosis purposes, especially in the pandemic. The respiratory sound classification task has been realized with numerous deep neural network (DNN) models due to their superior performance. However, in the high-stake medical domain where decisions can have significant consequences, it is desirable to develop interpretable models; thus, providing understandable reasons for physicians and patients. To address the issue, we propose a prototype learning framework, that jointly generates exemplar samples for explanation and integrates these samples into a layer of DNNs. The experimental results indicate that our method outperforms the state-of-the-art approaches on the largest public respiratory sound database.
Keywords
- interpretable machine learning, prototype-based explanation, respiratory sound classification
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Electrical and Electronic Engineering
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2022 IEEE International Conference on Acoustics, Speech, and Signal Processing: ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 9087-9091 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2022-May).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Prototype Learning for Interpretable Respiratory Sound Analysis
AU - Ren, Zhao
AU - Nguyen, Thanh Tam
AU - Nejdl, Wolfgang
N1 - Funding Information: Acknowledgments. This research was funded by the Federal Ministry of Education and Research (BMBF), Germany under the project LeibnizKILabor with grant No. 01DD20003.
PY - 2022/5
Y1 - 2022/5
N2 - Remote screening of respiratory diseases has been widely studied as a non-invasive and early instrument for diagnosis purposes, especially in the pandemic. The respiratory sound classification task has been realized with numerous deep neural network (DNN) models due to their superior performance. However, in the high-stake medical domain where decisions can have significant consequences, it is desirable to develop interpretable models; thus, providing understandable reasons for physicians and patients. To address the issue, we propose a prototype learning framework, that jointly generates exemplar samples for explanation and integrates these samples into a layer of DNNs. The experimental results indicate that our method outperforms the state-of-the-art approaches on the largest public respiratory sound database.
AB - Remote screening of respiratory diseases has been widely studied as a non-invasive and early instrument for diagnosis purposes, especially in the pandemic. The respiratory sound classification task has been realized with numerous deep neural network (DNN) models due to their superior performance. However, in the high-stake medical domain where decisions can have significant consequences, it is desirable to develop interpretable models; thus, providing understandable reasons for physicians and patients. To address the issue, we propose a prototype learning framework, that jointly generates exemplar samples for explanation and integrates these samples into a layer of DNNs. The experimental results indicate that our method outperforms the state-of-the-art approaches on the largest public respiratory sound database.
KW - interpretable machine learning
KW - prototype-based explanation
KW - respiratory sound classification
UR - http://www.scopus.com/inward/record.url?scp=85128155466&partnerID=8YFLogxK
U2 - https://doi.org/10.48550/arXiv.2110.03536
DO - https://doi.org/10.48550/arXiv.2110.03536
M3 - Conference contribution
AN - SCOPUS:85128155466
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 9087
EP - 9091
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -