Mitigating Stethoscope-Induced Shortcuts in Respiratory Sound Classification under Federated Domain Generalization with Causality-Inspired Interventions
Abstract
AI-driven respiratory sound classification (RSC) is promising for automated pulmonary disease detection, yet multi-site deployment is hindered by inter-stethoscope variability. We introduce a federated domain generalization (FedDG) formulation for RSC under stethoscope-induced device shifts, where clients use heterogeneous devices and the model is evaluated on unseen devices. Our empirical analysis shows that stethoscope-induced style and disease-specific content are tightly entangled, making deterministic style removal unreliable. In response, we propose a causality-inspired multimodal FedDG framework that combines: (i) a causality-inspired device style intervention network that performs content-preserving style perturbations, (ii) counterfactual text augmentation that neutralizes metadata shortcuts, and (iii) gradient alignment that facilitates device-invariant representations across clients. Built on a multimodal language-audio pretraining model, it outperforms conventional data augmentation and federated learning baselines in leave-one-device-out validation on ICBHI and SPRSound datasets. Code will be released upon publication.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of a causality-inspired multimodal federated domain generalization framework for respiratory sound classification, which effectively mitigates stethoscope-induced biases and enhances model robustness across heterogeneous devices. The technical contributions are substantial, offering a new lens through which to view the challenges of audio classification in medical contexts, thereby advancing the field significantly.
Methodology
The proposed methodology introduces a novel federated domain generalization framework specifically tailored for respiratory sound classification, addressing the critical issue of inter-stethoscope variability. The integration of a causality-inspired device style intervention network, counterfactual text augmentation, and gradient alignment represents a significant advancement in the field, as it not only tackles the entanglement of device style and disease content but also enhances the robustness of the model across heterogeneous devices. The approach is well-structured, leveraging causal inference principles to inform data augmentation strategies, which is a fresh perspective in the context of audio classification.
Results & evaluation
The experimental setup is robust, utilizing two well-defined datasets (ICBHI and SPRSound) and employing leave-one-device-out validation to rigorously assess the model's performance. The results demonstrate that the proposed method consistently outperforms conventional data augmentation and federated learning baselines, indicating its effectiveness in improving cross-device generalization. The ablation studies further substantiate the contributions of each component of the framework, providing clear evidence for the importance of the causality-inspired interventions.
Reproducibility
While the paper mentions that code will be released upon publication, the absence of a current project URL limits immediate reproducibility. The methodology is described in sufficient detail to allow for replication, but access to the code and datasets would be essential for full verification of results.
Limitations
One limitation is the reliance on specific datasets, which may not fully capture the diversity of respiratory sound recordings across different clinical settings. Additionally, the paper acknowledges the need for future work to address privacy concerns and computational efficiency in federated learning settings, which are critical for real-world applications.
Broader impact
The framework has significant potential implications for telemedicine and automated pulmonary disease detection, particularly in enhancing the reliability of AI-driven diagnostics across various healthcare environments. By addressing device-induced biases, the work contributes to the broader goal of equitable healthcare access and improved patient outcomes. The main contribution of this paper is the introduction of a causality-inspired multimodal federated domain generalization framework for respiratory sound classification, which effectively mitigates stethoscope-induced biases and enhances model robustness across heterogeneous devices. The technical contributions are substantial, offering a new lens through which to view the challenges of audio classification in medical contexts, thereby advancing the field significantly.
Author of this paper? Send a correction.