Contextual Discrepancy-Aware Contrastive Learning for Robust Medical Time Series Diagnosis in Small-Sample Scenarios

• URL: http://arxiv.org/abs/2601.07548v1
• Date: Mon, 12 Jan 2026 13:54:50 GMT
• Title: Contextual Discrepancy-Aware Contrastive Learning for Robust Medical Time Series Diagnosis in Small-Sample Scenarios
• Authors: Kaito Tanaka, Aya Nakayama, Masato Ito, Yuji Nishimura, Keisuke Matsuda,
• Abstract summary: CoDAC (Contextual Discrepancy-Aware Contrastive learning) is a novel framework that enhances diagnostic accuracy and generalization.<n>CoDAC leverages external healthy data and introduces a Contextual Discrepancy Estimator (CDE) built upon a Transformer-based Autoencoder.<n>Experiments on Alzheimer's Disease EEG, Parkinson's Disease EEG, and Myocardial Infarction ECG datasets demonstrate CoDAC's superior performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical time series data, such as EEG and ECG, are vital for diagnosing neurological and cardiovascular diseases. However, their precise interpretation faces significant challenges due to high annotation costs, leading to data scarcity, and the limitations of traditional contrastive learning in capturing complex temporal patterns. To address these issues, we propose CoDAC (Contextual Discrepancy-Aware Contrastive learning), a novel framework that enhances diagnostic accuracy and generalization, particularly in small-sample settings. CoDAC leverages external healthy data and introduces a Contextual Discrepancy Estimator (CDE), built upon a Transformer-based Autoencoder, to precisely quantify abnormal signals through context-aware anomaly scores. These scores dynamically inform a Dynamic Multi-views Contrastive Framework (DMCF), which adaptively weights different temporal views to focus contrastive learning on diagnostically relevant, discrepant regions. Our encoder combines dilated convolutions with multi-head attention for robust feature extraction. Comprehensive experiments on Alzheimer's Disease EEG, Parkinson's Disease EEG, and Myocardial Infarction ECG datasets demonstrate CoDAC's superior performance across all metrics, consistently outperforming state-of-the-art baselines, especially under low label availability. Ablation studies further validate the critical contributions of CDE and DMCF. CoDAC offers a robust and interpretable solution for medical time series diagnosis, effectively mitigating data scarcity challenges.

Related papers

• MIRNet: Integrating Constrained Graph-Based Reasoning with Pre-training for Diagnostic Medical Imaging [67.74482877175797]
  MIRNet is a novel framework that integrates self-supervised pre-training with constrained graph-based reasoning.<n>We introduce TongueAtlas-4K, a benchmark comprising 4,000 images annotated with 22 diagnostic labels.
  arXiv  Detail & Related papers  (2025-11-13T06:30:41Z)
• Adapting HFMCA to Graph Data: Self-Supervised Learning for Generalizable fMRI Representations [57.054499278843856]
  Functional magnetic resonance imaging (fMRI) analysis faces significant challenges due to limited dataset sizes and domain variability between studies.<n>Traditional self-supervised learning methods inspired by computer vision often rely on positive and negative sample pairs.<n>We propose adapting a recently developed Hierarchical Functional Maximal Correlation Algorithm (HFMCA) to graph-structured fMRI data.
  arXiv  Detail & Related papers  (2025-10-05T12:35:01Z)
• Self-Supervised Cross-Encoder for Neurodegenerative Disease Diagnosis [6.226851122403944]
  We propose a novel self-supervised cross-encoder framework that leverages the temporal continuity in longitudinal MRI scans for supervision.<n>This framework disentangles learned representations into two components: a static representation, constrained by contrastive learning, which captures stable anatomical features; and a dynamic representation, guided by input-gradient regularization, which reflects temporal changes.<n> Experimental results on the Alzheimer's Disease Neuroimaging Initiative dataset demonstrate that our method achieves superior classification accuracy and improved interpretability.
  arXiv  Detail & Related papers  (2025-09-09T11:52:24Z)
• Conditional Electrocardiogram Generation Using Hierarchical Variational Autoencoders [0.0]
  We propose a conditional Nouveau VAE model for ECG signal generation (cNVAE-ECG)<n>This paper proposes the publicly available conditional Nouveau VAE model for ECG signal generation (cNVAE-ECG)
  arXiv  Detail & Related papers  (2025-03-03T13:30:36Z)
• Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration [8.358846277772779]
  Traditional interpretation of Intravascular Ultrasound (IVUS) images during Percutaneous Coronary Intervention ( PCI) is time-intensive and inconsistent.<n>A parallel 2D U-Net model with a multi-stage segmentation architecture has been developed to enable secure data analysis across institutions.<n>A Dice Similarity Coefficient (DSC) of 0.706, the model effectively identifies plaques and detects circular boundaries in real-time.
  arXiv  Detail & Related papers  (2024-12-19T13:06:28Z)
• FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Data [52.55123685248105]
  Cardiovascular diseases (CVDs) are currently the leading cause of death worldwide, highlighting the critical need for early diagnosis and treatment. Machine learning (ML) methods can help diagnose CVDs early, but their performance relies on access to substantial data with high quality. This paper presents the first real-world FL benchmark for cardiovascular disease detection, named FedCVD.
  arXiv  Detail & Related papers  (2024-10-28T02:24:01Z)
• Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series [12.469204999759965]
  We present COMET, an innovative hierarchical framework that leverages data consistencies at all inherent levels in medical time series. Our meticulously designed model systematically captures data consistency from four potential levels: observation, sample, trial, and patient levels. We compare COMET against six baselines using three diverse datasets, which include ECG signals for myocardial infarction and EEG signals for Alzheimer's and Parkinson's diseases.
  arXiv  Detail & Related papers  (2023-10-21T13:59:31Z)
• Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training [75.40980802817349]
  Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. We introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites.
  arXiv  Detail & Related papers  (2023-08-31T00:36:10Z)
• K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment [71.27193056354741]
  The problem of how to assess cross-modality medical image synthesis has been largely unexplored. We propose a new metric K-CROSS to spur progress on this challenging problem. K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
  arXiv  Detail & Related papers  (2023-07-10T01:26:48Z)
• Semi-Supervised Learning for Multi-Label Cardiovascular Diseases Prediction:A Multi-Dataset Study [17.84069222975825]
  Current ECG-based diagnosis systems show promising performance owing to the rapid development of deep learning techniques. Label scarcity problem, the co-occurrence of multiple CVDs and the poor performance on unseen datasets hinder the widespread application of deep learning-based models. We propose a multi-label semi-supervised model (ECGMatch) to recognize multiple CVDs simultaneously with limited supervision.
  arXiv  Detail & Related papers  (2023-06-18T07:46:19Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.