{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/patient-aware-feature-alignment-for-robust","title":"Patient-Aware Feature Alignment for Robust Lung Sound Classification:Cohesion-Separation and Global Alignment Losses","arxiv_id":"2505.23834","date":"2025-05-28","proceeding":null,"authors":["Seung Gyu Jeong","Seong Eun Kim"],"abstract":"Lung sound classification is vital for early diagnosis of respiratory diseases. However, biomedical signals often exhibit inter-patient variability even among patients with the same symptoms, requiring a learning approach that considers individual differences. We propose a Patient-Aware Feature Alignment (PAFA) framework with two novel losses, Patient Cohesion-Separation Loss (PCSL) and Global Patient Alignment Loss (GPAL). PCSL clusters features of the same patient while separating those from other patients to capture patient variability, whereas GPAL draws each patient's centroid toward a global center, preventing feature space fragmentation. Our method achieves outstanding results on the ICBHI dataset with a score of 64.84\\% for four-class and 72.08\\% for two-class classification. These findings highlight PAFA's ability to capture individualized patterns and demonstrate performance gains in distinct patient clusters, offering broader applications for patient-centered healthcare.","url_abs":"https://arxiv.org/abs/2505.23834v1","url_pdf":"https://arxiv.org/pdf/2505.23834v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"patient-aware-feature-alignment-for-robust","repo_url":"https://github.com/wa976/pafa","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"audio-classification","task_name":"Audio Classification"},{"task_slug":"lung-sound-classification","task_name":"Lung Sound Classification"},{"task_slug":"sound-classification","task_name":"Sound Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/audio-classification-on-icbhi-respiratory","task":"Audio Classification","dataset":"ICBHI Respiratory Sound Database","model":"BEATs (PAFA)","rank_in_archive_order":2,"of":25,"metrics":{"ICBHI Score":"64.84","Sensitivity":"47.63","Specificity":"82.05"},"uses_additional_data":true},{"leaderboard":"/sota/audio-classification-on-icbhi-respiratory","task":"Audio Classification","dataset":"ICBHI Respiratory Sound Database","model":"BEATs (CE)","rank_in_archive_order":4,"of":25,"metrics":{"ICBHI Score":"63.49","Sensitivity":"48.21","Specificity":"78.77"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2505.23834","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}