Yuxing Lu, Xukai Zhao, J. Ben Tamo, Micky C. Nnamdi, Rui Peng, Shuang Zeng, Xingyu Hu, Jinzhuo Wang, May Dongmei Wang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Sleep disorders, particularly Obstructive Sleep Apnea (OSA), have a considerable effect on an individual's health and quality of life. Accurate sleep stage classification and prediction of OSA are crucial for timely diagnosis and effective management of sleep disorders. In this study, we develop a sequential network that enhances sleep stage classification by incorporating self-attention mechanisms and Conditional Random Fields (CRF) into a deep learning model comprising multi-kernel Convolutional Neural Networks (CNNs) and Transformer-based encoders. The self-attention mechanism enables the model to focus on the most discriminative features extracted from single-channel electroencephalography (EEG) recordings, while the CRF module captures the temporal dependencies between sleep stages, improving the model's ability to learn more plausible sleep stage sequences. Moreover, we explore the relationship between sleep stages and OSA severity by utilizing the predicted sleep stage features to train various regression models for Apnea-Hypopnea Index (AHI) prediction. Our experiments demonstrate an improved sleep stage classification performance of 78.7%, particularly on datasets with diverse AHI values, and highlight the potential of leveraging sleep stage information for monitoring OSA. By employing advanced deep learning techniques, we thoroughly explore the intricate relationship between sleep stages and sleep apnea, laying the foundation for more precise and automated diagnostics of sleep disorders.

Traditional convolutional neural networks (tCNNs) often struggle with medical image analysis due to complex transformations and irregular structures with weak boundaries. Deformable convolutional neural networks (dCNNs) address these challenges through specialized modules, showing improvements in classification, segmentation, and explainability on general image tasks. However, dCNNs remain relatively unexplored in the medical domain. This study provides a unique quantitative comparison of explainability between tCNNs and dCNNs on medical image classification tasks, focusing on lung disease classification using chest X-rays (CXRs). We tested four models with varying degrees of deformability and generated saliency maps using Guided GradCAM. To quantify interpretability, we introduced a novel metric: continuous intersection over union (cIoU). While tCNNs demonstrated slightly better classification results, dCNNs showed significant improvements in explainability. We observed a positive correlation between the number of deformable layers in a model and the quality of its saliency maps. These findings highlight the potential of dCNNs in enhancing the interpretability of medical image analysis, which is crucial for real-world clinical research and practice.

Advanced deep-learning techniques have been employed to develop clinical decision support systems for diagnosis and prognosis using medical images. However, the presence of out-of-distribution (OOD) samples, which deviate from the training data distribution, poses a significant challenge. Accurate quantification of the predictive uncertainty is crucial for ensuring reliable and dependable implementation in medical settings as a clinical decision support system. In this work, we propose an ensemble model to derive predictive uncertainty estimates for uncertainty quantification on OOD medical imaging. Specifically, the models are initialized with ImageNet pre-trained weights and fine-tuned on chest Computed Tomography (CT). Moreover, we utilize Grad-CAM to visualize and interpret the areas of the image that contribute most to the model’s predictions and uncertainty estimates. This visualization technique enhances the in-terpretability of our ensemble model and supports more informed clinical decision-making. Through extensive experiments on three Chest CT datasets, we have demonstrated the effectiveness of our approach in estimating uncertainty under domain shifting. Our results provide valuable insights into the reliability and specificity of deep ensemble uncertainty predictions in medical image analysis. Our Uncertainty-Aware Ensemble (UAE) approach can enable reliable and transparent predictions for safety-critical medical applications.