arXiv：cs.AI· Yi Tian, Xiaohuan Chen, Yufei tang, Xinyu Lin, Hongliang Li, Chengbin Hou, Degen Huang, Rongshan Yu·· 10 小时前
RespFocus：用多实例学习为弱监督呼吸音分类提供可检验的时间证据
RespFocus: Learning Temporal Evidence for Weakly Supervised Respiratory Sound Classification
AI 导读
针对噪声临床录音中细湿啰音与持续哮鸣音难以识别、深度模型又缺乏时间证据可解释性的问题，研究者提出弱监督框架 RespFocus，通过多实例学习从周期级标签中习得可检验的时间证据并融入分类。
正文
Abstract:Respiratory auscultation provides a low-cost means of assessing pulmonary function, yet transient crackles and sustained wheezes can be difficult to recognize in noisy clinical recordings. Conventional deep learning classifiers often operate as black boxes, offering limited insight into the temporal acoustic evidence supporting their predictions. We present RespFocus, a weakly supervised framework that uses multiple instance learning (MIL) to learn inspectable temporal evidence from cycle-level labels and integrate it into classification. We also introduce PedResp2026, a multicenter pediatric respiratory sound cohort collected in routine clinical environments with adjudicated cycle-level annotations. In classification experiments, RespFocus achieves four-class Scores of 69.55\% on public ICBHI 2017 and 72.91\% on PedResp2026. Evidence diagnostics and matched temporal interventions support the functional contribution of the learned evidence to model predictions. Together, these findings highlight RespFocus's potential as a tool for automated respiratory sound diagnosis.
| Comments: | 9 pages, 5 figures,2 tables |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.15702 [cs.LG] |
| (or arXiv:2610.15702v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.15702 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hongliang Li [view email]
[v1]
Sun, 11 Oct 2026 15:37:56 UTC (3,947 KB)
来源：arXiv：cs.AI · arxiv.org