Title: DAAC: Discrepancy-Aware Adaptive Contrastive Learning for Medical Time series
Venue: NeurIPS 2025
Accepted: yes
Keywords: Medical Time Series

Contrastive Learning

Multi-View Representation

Discrepancy Estimation

Disease Diagnosis
Abstract: Medical time-series data play a vital role in disease diagnosis but suffer from limited labeled samples and single-center bias, which hinder model generalization and lead to overfitting. To address these challenges, we propose DAAC (Discrepancy-Aware Adaptive Contrastive learning), a learnable multi-view contrastive framework that integrates external normal samples and enhances feature learning through adaptive contrastive strategies. DAAC consists of two key modules: (1) a Discrepancy Estimator, built upon a GAN-enhanced encoder-decoder architecture, captures the distribution of normal data and computes reconstruction errors as indicators of abnormality. These discrepancy features augment the target dataset to mitigate overfitting. (2) an Adaptive Contrastive Learner uses multi-head attention to extract discriminative representations by contrasting embeddings across multiple views and data granularities (subject, trial, epoch, and temporal levels), eliminating the need for handcrafted positive-negative sample pairs. Extensive experiments on three clinical datasets—covering Alzheimer’s disease, Parkinson’s disease, and myocardial infarction—demonstrate that DAAC significantly outperforms existing methods, even when only 10\% of labeled data is available, showing strong generalization and diagnostic performance. Our code is available
at https://github.com/CUHKSZ-MED-BioE/DAAC.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces DAAC (Discrepancy-Aware Adaptive Contrastive Learning), a framework aimed at enhancing the generalization of medical time-series models, particularly when labeled data is scarce and collected from single centers. DAAC integrates two core modules: a Discrepancy Estimator, which learns the distribution of normal samples using a GAN-enhanced encoder-decoder and calculates reconstruction errors to represent abnormality, and an Adaptive Contrastive Learner, which leverages multi-head attention to contrast multiple data views (subject, trial, epoch, and temporal) without relying on handcrafted positive/negative sample pairs. This design mitigates overfitting, supports robust representation learning, and improves model performance in downstream diagnostic tasks.\nExtensive experiments across three medical datasets—Alzheimer’s, Parkinson’s, and myocardial infarction—demonstrate that DAAC significantly outperforms existing baselines, even with only 10% labeled data. The method excels in both partial and full fine-tuning scenarios, achieving state-of-the-art results across multiple evaluation metrics. Visualizations of multi-view embeddings confirm the effectiveness of the contrastive design in capturing subject- and view-specific information. While DAAC already shows strong results, the authors acknowledge that its discrepancy estimation could be further refined for more precise integration of external knowledge, suggesting room for future improvements in balancing auxiliary data with target domain characteristics.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \nDAAC demonstrates notable strengths in addressing two major challenges in medical time-series analysis: limited labeled data and generalization beyond single-center datasets. By combining a GAN-based discrepancy estimator with a hierarchical contrastive learning framework, it effectively leverages abundant external normal samples without requiring handcrafted contrastive pairs. The model incorporates subject, trial, epoch, and temporal levels in its contrastive objective, resulting in robust, multiscale representations. Extensive experiments across diverse disease datasets show that DAAC consistently outperforms state-of-the-art baselines, even when using only 10% of labeled data. Its modular design and strong empirical results underscore its practicality for real-world clinical deployment in low-resource settings.\nWeaknesses: \nDespite its strengths, DAAC has certain limitations. The use of a single reconstruction-based discrepancy signal from external normal data may provide only a coarse representation of abnormality, which could limit the precision of feature augmentation. While the model avoids manual pairing in contrastive learning, the multi-level and multi-view loss architecture introduces significant complexity in training, requiring careful tuning of multiple hyperparameters. Additionally, the potential for distributional mismatch between external normal data and target patient data may still affect generalization if not managed properly. Future work is needed to refine discrepancy modeling and explore more adaptive ways to balance external and target domain knowledge.\n\nQUESTIONS:\nQ1) The proposed method was tested on only 2 medical modalities, EEG and ECG. What are the next target modalities in the medical domain?\nQ2) How about outside of the medical domain?\n\nLIMITATIONS:\nThe authors state that “We explicitly discuss limitations in Section 6”, but the main text ends with “5 Conclusion”. Where is the section 6?", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe manuscript introduces DAAC, a learnable multi-view contrastive framework that leverages an external set of normal (i.e., healthy) samples and an adaptive contrastive strategy to strengthen representation learning. Extensive experiments on three clinically heterogeneous datasets—Alzheimer's disease (AD), Parkinson's disease (PD), and myocardial infarction (MI)—demonstrate that DAAC consistently outperforms competing methods, even when only 10 % of the target data are labelled.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n\n1. Broad empirical validation: Experiments span three distinct modalities and disease types, and the proposed method improves average performance in every setting.\n\n2. Label-efficient representation learning: DAAC obtains contrastive embeddings without requiring additional labels, facilitating deployment in domains where annotation is costly.\n\n3. Comprehensive baselines: The study benchmarks against several strong methods, providing a clear view of relative gains.\n\n### Weaknesses\n\n1. Scale of the external normal set\n\n   The paper repeatedly refers to a “large-scale” normal pool, yet its concrete size and the sensitivity of downstream performance to that size remain unclear. Given that gathering sizeable healthy cohorts is not always trivial in medical contexts, an ablation that varies normal-set size (e.g., 1 k → 5 k → 10 k subjects) and quantifies its effect on target-task metrics would greatly clarify practical requirements.\n\n2. Assumption on reconstruction error (L 139 - 140)\n\n   The text states that the reconstruction error E is “smaller for healthy samples … and larger for abnormal samples.” This hypothesis should be verified experimentally—e.g., by plotting the error distributions for normal vs. abnormal cases or by reporting AUROC when E is used as an anomaly detector.\n\n3. Architectural transparency\n\n    Details of the encoder/decoder designs (layer counts, filter sizes, activation functions, parameter counts) and the reasoning behind their selection are missing. Providing a table or schematic—ideally with a brief discussion of alternatives considered—would improve reproducibility.\n\nQUESTIONS:\n### Questions\n\n1. Operationalising “large-scale” normal set\n\n   Please define the absolute size (number of subjects / images) and class balance of the external normal pool used in each experiment. In addition, supply an ablation (e.g., 10 %, 25 %, 50 %, 100 % of your current pool) showing how downstream performance varies with normal-set size.\n\n    Criteria: If ablation shows (i) the minimal size needed to reach ≥95 % of the reported gain, and (ii) performance degrades gracefully when data are scarce, I will view scalability concerns as addressed and raise my score. Missing or inconclusive evidence would lower confidence.\n\n2. Empirical verification of the reconstruction-error assumption (L139-140) \n\n    Provide quantitative evidence (e.g., AUROC, distribution plots) that healthy samples indeed yield lower reconstruction error E than abnormal samples across all datasets.\n\n   Criteria: In all three data sets, the distribution of normal and abnormal reconstruction errors is shown, supporting what is claimed in L139-140.\n\n3. Architectural transparency and rationale\n\n    Supply a concise table (layers, filter sizes, activation functions, parameter count) for both encoder and decoder, plus a short justification for key design choices (e.g., depth, latent dimension).\n\n    Criteria: Providing full specs and reasoning (or result of preliminary experiments to determine the architecture) would resolve this weakness and may improve my reproducibility criterion.\n\n-----\n\n### Minor points.\n\n- The left-hand subplot is labeled “surpervised.” Please correct this to “supervised.”\n- Formatting of Table 2.: What purpose does the boldface serve? Since an asterisk (*) already denotes the proposed method, using bold to mark top performance may confuse readers—particularly when multiple entries appear in bold.\n\nLIMITATIONS:\nNo. Although the manuscript outlines several methodological constraints, it omits two practical limitations that may affect real-world adoption:\n\n- Data-collection burden for large normal cohorts. DAAC relies on a sizeable external pool of healthy cases, yet assembling such cohorts can be logistically demanding and financially expensive. Quantifying the sensitivity of performance to normal-set size or discussing data-sharing partnerships would clarify feasibility.\n\n- Privacy considerations for normal data. Even “healthy” clinical data can contain identifiable information. A brief discussion of de-identification, consent, and compliance with regulations (e.g., HIPAA, GDPR) would strengthen the societal-impact analysis.\n\nAddressing these points would provide a more balanced view of the framework's limitations and ethical implications.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper proposes DAAC (Discrepancy-Aware Adaptive Contrastive learning), a novel framework designed to address the challenges of limited labeled data and single-center bias in medical time-series analysis. It has two core components: a Discrepancy Estimator  and  an Adaptive Contrastive Learner. Extensive experiments on three clinical datasets Alzheimer’s disease, Parkinson’s disease, and myocardial infarction demonstrate that DAAC achieves superior generalization and diagnostic performance, particularly under low-label regimes.

STRENGTHS AND WEAKNESSES:
Strength
The paper is focusing on a very important domain
The experiments demonstrating fine-tuning for downstream classification tasks are comprehensively presented in the paper.
The proposed method demonstrates strong performance in downstream classification tasks, even with limited labeled data.

Weakness
There are many terms referring many kind and source of data, including normal data/external data/target data/original data, which is quite confusing. It makes the functionality of the Discrepancy Estimator unclear. 
The experimental setup is not clear, somehow confusing.
There are many abbreviations  has no full name, especially for the ablations, which makes the effectiveness of each part very hard to interpret.

QUESTIONS:
From my understanding the discrepancy is just an augmented feature of the original data, so why do you choose discrepancy as this additional fearture? what about other metrics like the data point to the cluster of normal/disease data? There should be some intuitions. 
Which part do you think contribute the most to the final improvement?
Can you define kind and source of data, including normal data/external data/target data/original data

LIMITATIONS:
As above
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a framework, Discrepancy-Aware Adaptive Contrastive learning (DAAC), for medical time-series classification, aiming to address challenges of limited labeled data and single-center bias. The complete framework involves pre-training a Discrepancy Estimator, using it to augment the target dataset, training the Adaptive Contrastive Learner in a self-supervised manner, and finally, fine-tuning for downstream diagnostic tasks. The authors demonstrate the effectiveness of DAAC on three clinical datasets (Alzheimer's, Parkinson's, and myocardial infarction), showing that it outperforms existing methods, especially in low-label scenarios.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n-The method is validated on three distinct clinical datasets (Alzheimer's, Parkinson's, and Myocardial Infarction), demonstrating its applicability across different medical time-series modalities (EEG, ECG).\n\n-The experiments are robust, testing the model's performance in both high-label (100%) and, crucially, low-label (10%) scenarios, which directly supports the paper's core motivation.\n\n-The inclusion of comprehensive ablation studies provides strong evidence for the contribution of each component of the proposed loss function. Furthermore, a mutual information analysis validates the utility of the proposed discrepancy feature, showing it is more informative than any of the original features.\n\n-The concepts of inter-view and intra-view contrastive learning are well-motivated and supported by compelling UMAP visualizations (Figure 3) that show the learned representations have the desired properties of being separable between views and discriminative between subjects.\n\n-The proposed method of leveraging large, external \"normal\" datasets to mitigate overfitting and single-center bias is a promising direction for improving model generalization.\n\n-The core contribution of the Discrepancy Estimator—using the reconstruction error from a GAN-style model trained on external normal data as an explicit feature—is an original and clever way to encode and transfer knowledge about the \"healthy\" state.\n\nWeaknesses\n\n-The dataset choices seem slightly arbitrary especially considering the fact that the medical timeseries domain has mature datasets like MIMIC with tens of thousands of papers. \n\n-The overall loss function is a weighted combination of multiple terms, and the authors state they used a fixed weighting scheme without tuning. While this may show generality, the paper lacks an analysis of the model's sensitivity to these hyperparameters. It is unclear how performance might change with different weightings, which could be important for applying the method to new datasets.\n\n-The proposed three-stage training process appears computationally expensive, requiring the pre-training of two separate models before final fine-tuning. The paper does not provide details on training times or overall computational cost, which is a minor weakness for reproducibility and assessing practical deployment feasibility.\n\n-The authors acknowledge that using a single sequence-level reconstruction error as the discrepancy feature is \"somewhat coarse\". This simplification might discard finer-grained temporal information about a sample's abnormality.\n\n-The naming of models in the results tables (e.g., \"ACL*\", \"COMET DE*\", \"DAAC*\") could be clearer. While the text implies their relationships, an explicit definition in the table captions would remove ambiguity for the reader trying to map the components of the ablation study to the final model.\n\n-A key component, the Discrepancy Estimator, is contingent on the availability of a large, external dataset of purely normal subjects. This may not always be a feasible assumption, particularly for rare diseases or due to data-sharing restrictions. The paper does not explore the method's behavior if such a dataset is unavailable or small.\n\n-The method assumes the external normal data and the target data \"share similar characteristics\". It does not address the potential risk of negative transfer if there is a significant domain shift between the source of the external data and the target data, which could cause the discrepancy feature to be misleading.\n\n-The work builds heavily upon the hierarchical contrastive learning structure (subject, trial, epoch, temporal) from a recent paper, COMET. The paper is transparent about this, but it means the novelty lies in the two additions—the discrepancy estimator and the MHA-based adaptive views—rather than the entire hierarchical framework.\n\nQUESTIONS:\n1. Regarding the sensitivity of the contrastive loss weights: The overall loss function is a weighted sum of five components with a fixed weight ratio of 1:1:1:1:2. You state that these weights were not tuned to demonstrate the method's generality. However, this choice warrants more justification. Could you please provide some intuition or an ablation study on the effect of these weights? For example, what is the reasoning for weighting the view-level loss (L_V) twice as much as the hierarchical losses? A sensitivity analysis on one of the datasets, showing how performance varies with different weightings, would significantly strengthen the paper by demonstrating the robustness of this design choice. This would increase my confidence that the method can be readily applied to new datasets without extensive tuning.\n\n2. Regarding the risk of negative transfer from external data: The Discrepancy Estimator relies on an external dataset of normal subjects to learn a representation of \"health.\" This is a powerful idea but hinges on the assumption that the external and target datasets \"share similar characteristics\". Could you please discuss the potential risk of negative transfer if there is a significant domain shift between the external and target data (e.g., different sensor hardware, recording protocols, or patient demographics)? A discussion of this limitation, or ideally an experiment showing the method's performance when the external data source is intentionally mismatched (e.g., from a different public dataset), would be really useful about the practical deployment of this method.\n\n3. The design of the discrepancy feature: The choice to compute a single sequence-level reconstruction error as the discrepancy feature is justified by arguing it is more robust to noise than point-wise errors. While intuitive, this compresses a potentially rich error signal into a single scalar. Could you provide further justification for this design choice? For instance, did you experiment with alternative representations, such as a vector of channel-wise errors or a smoothed time-series of the reconstruction error itself? \n\n4. Were other more established datasets like MIMIC considered in the training and evaluation of this method?\n\nLIMITATIONS:\nThe authors have made a commendable effort to be upfront about the limitations of their work. In the conclusion, they explicitly state that the discrepancy estimation is \"suboptimal\" and that the representation of discrepancy as a single value is \"somewhat coarse\". Furthermore, in their own checklist, they acknowledge that they did not provide detailed information about computational resources needed for reproducibility.\n\nA key point to address would be the fairness and equity implications of the Discrepancy Estimator. Since the model's performance relies on an external dataset of \"normal\" subjects, it is critical to discuss the potential for bias if this external dataset is not demographically representative (e.g., in terms of age, sex, or ethnicity). For instance, the model could perform differently for underrepresented groups, leading to diagnostic disparities. Acknowledging this potential and discussing how to select or audit external datasets for fairness would significantly strengthen the paper's treatment of its societal impact.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-04"}