mlsanityData to 14 Oct 2026

Tri-MTL: A Triple Multitask Learning Approach for Respiratory Disease Diagnosis

arXiv 2505.06271 ↗First posted 6 May 2025cs.LG cs.AI cs.SDcited by 3

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Abstract (arXiv)

Auscultation remains a cornerstone of clinical practice, essential for both initial evaluation and continuous monitoring. Clinicians listen to the lung sounds and make a diagnosis by combining the patient's medical history and test results. Given this strong association, multitask learning (MTL) can offer a compelling framework to simultaneously model these relationships, integrating respiratory sound patterns with disease manifestations. While MTL has shown considerable promise in medical applications, a significant research gap remains in understanding the complex interplay between respiratory sounds, disease manifestations, and patient metadata attributes. This study investigates how integrating MTL with cutting-edge deep learning architectures can enhance both respiratory sound classification and disease diagnosis. Specifically, we extend recent findings regarding the beneficial impact of metadata on respiratory sound classification by evaluating its effectiveness within an MTL framework. Our comprehensive experiments reveal significant improvements in both lung sound classification and diagnostic performance when the stethoscope information is incorporated into the MTL architecture.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 33 references that are in the map. First posted on arXiv 6 May 2025.

Builds on

16 of 33 references.

References · 16

  1. 2017An Overview of Multi-Task Learning in Deep Neural Networkscited by 803
  2. 2021AST: Audio Spectrogram Transformercited by 643
  3. 2019CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Gradingcited by 35
  4. 2020Multi-Task Learning for Dense Prediction Tasks: A Surveycited by 28
  5. 2020RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Settingcited by 26
  6. 2023Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classificationcited by 24
  7. 2023Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classificationcited by 16
  8. 2022Pretraining Respiratory Sound Representations using Metadata and Contrastive Learningcited by 15
  9. 2019A Feature Transfer Enabled Multi-Task Deep Learning Model on Medical Imagingcited by 14
  10. 2017A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classificationcited by 13
  11. 2024RepAugment: Input-Agnostic Representation-Level Augmentation for Respiratory Sound Classificationcited by 12
  12. 2024BTS: Bridging Text and Sound Modalities for Metadata-Aided Respiratory Sound Classificationcited by 12
4 more
  1. 2023Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalancecited by 11
  2. 2024Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Recordscited by 5
  3. 2023Cross-Task Attention Network: Improving Multi-Task Learning for Medical Imaging Applicationscited by 4
  4. 2024Multi-Task Learning for Lung sound & Lung disease classificationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classificationcited by 0
  2. 2026Quality Adaptive Angular Margin Learning for Respiratory Sound Classificationcited by 0
  3. 2026Lung-SRAD: Spectral-Aware Regularized Audio DASS with Dual-Axis Patch-Mix Contrastive Learning for Respiratory Sound Classificationcited by 0