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

arXiv: https://arxiv.org/abs/2505.06271 · HTML: https://mlsanity.com/paper/2505.06271/

## 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 (most cited first, 16 of 16)

- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading](https://mlsanity.com/paper/1911.01376/) (2019)
- [Multi-Task Learning for Dense Prediction Tasks: A Survey](https://mlsanity.com/paper/2004.13379/) (2020)
- [RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting](https://mlsanity.com/paper/2011.00196/) (2020)
- [Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification](https://mlsanity.com/paper/2305.14032/) (2023)
- [Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification](https://mlsanity.com/paper/2312.09603/) (2023)
- [Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning](https://mlsanity.com/paper/2210.16192/) (2022)
- [A Feature Transfer Enabled Multi-Task Deep Learning Model on Medical Imaging](https://mlsanity.com/paper/1906.01828/) (2019)
- [A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification](https://mlsanity.com/paper/1703.01025/) (2017)
- [RepAugment: Input-Agnostic Representation-Level Augmentation for Respiratory Sound Classification](https://mlsanity.com/paper/2405.02996/) (2024)
- [BTS: Bridging Text and Sound Modalities for Metadata-Aided Respiratory Sound Classification](https://mlsanity.com/paper/2406.06786/) (2024)
- [Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance](https://mlsanity.com/paper/2311.06480/) (2023)
- [Automated Multi-Task Learning for Joint Disease Prediction on Electronic Health Records](https://mlsanity.com/paper/2403.04086/) (2024)
- [Cross-Task Attention Network: Improving Multi-Task Learning for Medical Imaging Applications](https://mlsanity.com/paper/2309.03837/) (2023)
- [Multi-Task Learning for Lung sound & Lung disease classification](https://mlsanity.com/paper/2404.03908/) (2024)

## Built on by (most cited first, 3 of 3)

- [Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classification](https://mlsanity.com/paper/2604.24096/) (2026)
- [Quality Adaptive Angular Margin Learning for Respiratory Sound Classification](https://mlsanity.com/paper/2606.11915/) (2026)
- [Lung-SRAD: Spectral-Aware Regularized Audio DASS with Dual-Axis Patch-Mix Contrastive Learning for Respiratory Sound Classification](https://mlsanity.com/paper/2606.11922/) (2026)

## 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.