DAAC: Discrepancy-Aware Adaptive Contrastive Learning for Medical Time series
Authors: Yifan WANG
Hongfeng Ai
Ruiqi Li
Maowei Jiang
Quangao Liu
Jiahua Dong
Ruiyuan Kang
Alan Liang
Zihang Wang
Ruikai Liu
Cheng Jiang
Chenzhong Li
Venue: NeurIPS 2025 poster ()
Keywords: Medical Time Series
Contrastive Learning
Multi-View Representation
Discrepancy Estimation
Disease Diagnosis
Primary area: neuroscience_and_cognitive_science
TL;DR: A contrastive learning framework integrating discrepancy estimation and adaptive attention for medical time-series 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.

DAAC: Discrepancy-Aware Adaptive Contrastive Learning for Medical Time series
Yifan Wang"!,
Hongfeng Ai Ruiqi Li Maowei Jiang 3t
Quangao Liu?, Jiahua Dong", Ruiyuan Kang", Alan Liang? Zihang Wang?, Ruikai Liu", Cheng Jiang"), Chenzhong Li
School of Medicine, The Chinese University of Hong Kong, Shenzhen, Guangdong. 518172, P.R. China; 2 UCAS; Tsinghua University; 4 TII; MBZUAI; NUS "Equal contribution
*Project leader *Corresponding author
Abstract
Medical time-series data play vital role in discase diagnosis but suffer from limited labeled samples and single-center bias, which hinder model generalization and lead overfitting. To address these challenges, 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 GAN-enhanced encoder-decoder architecture, captures the distribution normal data and computes reconstruction errors as indicators 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 four clinical datasets covering Alzheimer's 's disease, Parkinson's 's disease, myocar- dial infarction and alcoholic liver disease demonstrate that DAAC significantly outperforms existing methods, even when only 10% of labeled data is available, showing strong generalization and diagnostic performance. Our code available at htttt:::/// thub. com/ Http/CCHKSZ-NNA MED MED- MED-SS----DD----B/AAAAA
propose DAAC (Discrepancy-Awe) -ware
Introduction
Medical diagnosis tasks, such Alzheimer's disease Parkinsonism require high precision and exhibit low tolerance for errors due to their critical impact on patient outcomes. In data-driven methods, time- series data is accordingly preferred provide comprehensive information to support the accurate modeling Rangapuram et al. (2018) Gao et al. [2025]. However, the time-series data used are
naturally complex with hierarchical architectures Brunekreef et al. 20224] In contrast, the reliable
labeled data is limited in its amount and diversity Wen et al. 2020 Torres et al. [2021]. as they are
often from the same center Liu al. [202T) Zhu al. [2024] These characteristies result two key
challenges: (1) difficulty in learning effective patterns and (2) increased risk of overfitting, which
undermines the generalizability of data-driven medical diagnosis models.
To facilitate better pattern extraction, domain knowledge has been used to structure time-series data across multiple levels Wang al. [2023) Brunekreef al. 2204]] Kiyasseh et al.) [202T] In addition, representation learning methods, particularly contrastive learning, are employed to derive more informative representations. Although these endeavors improve the performance of ML model,
the human bias introduced in data leveling and positive-negative pairing Liu et al. 2023 Hong and
Yang 2021 Hwang et
2022 in contrastive learning also constrain the model performance.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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the other hand, to mitigate overfitting, external normal samples have been introduced as auxiliary resources compensate for the limitations of training data and to alleviate the risk of "single-center bias" WW et al. [022]. Harding-Esch et al. 2018). We admit the contribution of previous attempts, and think about that (1) whether we can design a more robust contrastive learning algorithm to avoid the labeling expense and bias, to better capture the effective patterns. (2) whether we can better utilize the external resource to avoid overfitting due to the data limitation.
Accordingly, we propose Discrepancy-Aware Adaptive Contrastive learning (DAAC) for medical diagnosis tasks. It includes two modules:
(1) Discrepancy Estimator. Leveraging large-scale public normal data, we train a reconstructor based encoder-decoder architecture with GAN-style enhancement (AE-GAN). This reconstructor effectively models the distribution of normal samples. When encountering abnormal inputs, the resulting reconstruction distribution. This discrepancy signal supplements the limited abnormal data, provides additional supervision, and helps mitigate overfitting during training.
quantifies the discrepancy between the input and the learned normal
(2) Adaptive Contrastive Learner. Instead of relying on handcrafted positive-negative sample pairing, the learner employs Multi-Head Attention (MHA) to adaptively identify contrastive relationships. Specifically, it contrasts embeddings across attention heads (Inter- (Inter-View) and across different patients (Intra- View). This mechanism enables the model extract informative representations while reducing representational redundancy. The resulting features are further fine-tuned in supervised manner for downstream medical diagnosis tasks.
The experimental results demonstrate that our method outperforms the SOTA methods across four representative clinical datasets, covering myocardial infarction, Alzheimer's disease, Parkinson's s disease and alcoholic liver disease. Notably, still has decent performance when merely 10% of the data is labeled, which highlights the effectiveness of the DAAC design.
2 Related Work
Medical time series Medical diagnosis often relies on physiological time-series signals such as EEG (aavers et al. (1974), ECGLLin et al.] (2024). EMG et al.] (2024). and EOG [Tng et al. [2020), which reflect complex temporal dynamics. Unlike images or texts, these data exhibit subtle and hierarchical variations Liu et al. [2023), increasing modeling difficulty. To address this, various representation learning methods have been proposed. TS2Vec Yue et al. [0022] organizes data
across temporal scales. CLOCS Kiyasseh al. [2021 models spatial-temporal invariance, and
COMET Wang et al. [2023] leverages four-level hierarchy to improve representation granularity.
However, medical datasets are often limited to single-center collections with costly manual annota-
tions Xu et al. 2021 Zheng et al. 2023). leading to data scarcity and overfitting Luo et al. 2023)
Neumann et al. 2018
Supervised learning for time series Supervised methods have shown strong performance in medical diagnostics, such as U-Sleep for EEG staging Persslev et al. [2021]. L-SeqSleepNet for cross- night generalization (Phan et al. 2003). and Inception- networks for EOG classification Zeng
et al. 2025 J. To address annotation bottlenecks, techniques like SimGAN [Golany et al. 2020
generate realistic ECG signals, with GAN-based augmentation now mainstream for |CG|Berge et al.
[2023] and EEG An et al. [2025]. Yet, detecting rare events and adapting across domains remains
challenging Chen et al. 2022 Zhang et al. [2022] Jeon al. (2033 Chen 2024
with supervised
learning constrained by label imbalance and limited diversity.
Contrastive learning for time series Self-supervised Baaming-especiall) contrastive meth- ods-has become promising alternative for time-series modeling Zhang [2024]. By con-
structing positive and negative pairs through data augmentation Chen et al. [2020], Yue et al. [2022]. Eldele et al. [2023), [Luu and Chen [2024], these approaches learn discriminative features without
labels. For example, SoftCLT et al. [2023] introduces soft assignment to improve flexibility but sensitive to noise; CoC et al. 2018 predicts future representations but suffers from negative
sampling bias; TS-TCC [Elele et al. 2021 contrasts temporal and contextual views yet struggles
with dynamic or short sequences. Improving representation robustness in low-data regimes remains an open challenge.

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3 Proposed Method: DAAC
As discussed above, in order to better extract more effective feature extraction and also avoid model overfitting on limited data, DAAC improved the paradigm of contrastive learning + downstream fine-tuning with two sophisticatedly designed modules. (1) Diserepancy Estimator (Ep) To avoid overfitting on limited data, we embed the information of the external normal data distribution into an Encoder-Decoder architecture reconstructor via GAN-style training [Sec|3.I) which is named as Discrepancy Estimator (Ep). Because the well-trained reconstructor captures the normal data distribution, and thus the reconstruction error reflects discrepancies of abnormal samples from normal ones. Appendix X demonstrates this experiment in detail. Such a design, utilizing the complementary information from normal data to alleviate the limitation of the given target data. The calculated discrepancy is used as feature to augment the original data. (2) Adaptive Contrastive Learner (Ez)) To better capture effective features without further labeling (Sec. (322) we use the Multi-Head Attention (MHA) to embed features, which are sufficiently optimized through adaptively differentiating the embedding between attention heads and patients (inter-view and intra- view contrastive mechanisms, respectively). Meanwhile, features from diverse levels are used to provide more comprehensive information.
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Figure 1: Overview of our three-stage training framework. We first pretrain a Discrepancy Estimator (Ep) normal data to extract reconstruction-basel features. These features are then fused with raw inputs for contrastive representation learning with an MHA encoder. Finally, the learned encoder is fine-tuned with supervision for downstream tasks.
Training and inference of Discrepancy Estimator. A Discrepancy Estimator (Ep) ) is trained using data from normal patients in D', whose samples are expressed as x². Then, it is used to perform inference on all samples in D. whose samples are expressed as x,, the calculated overall reconstruction error is used as an additional feature. fused with the raw data, resulting in a feature- augmented dataset with the number of features increased from F to F + 1. Stage 2: Training of Adaptive Contrastive Learner (EL) An encoder Eg with a multi-head attention block embed the augmented dataset into feature space, with the consideration of diverse data level and diverse views. Respecively, The well-trained encoder generates series-level features h; E RT xC with C' dimensions, and view-level features gi RT'Vxo xd with V views and d dimensions for each view. Stage 3: Supervised fine-tuning These representations are then used to train the classifier for specific downstream medical diagnostic tasks.
As shown in Figure [ the training process consists of three stages. Stage 1:
3.1 Discrepancy Estimator
Although the target dataset D contains limited and non-diverse data, the massive normal data D' from other institutions are available. Although it varies from D. but both of them share similar characteristics, which accordingly provides complementary information for extracting better feature and alleviating overfitting on targeted dataset D. As mentioned above. we utilize the discrepancy to reflect the information of the normal dataset, an Encoder-Decoder Discrepancy Estimator (Ep) ) enhanced by GAN-style training is used to provide the function.
3.1.1 Encoder-Decoder Generator
The generator Gyen is composed of an encoder Egen and decoder D Dgen. which work together to learn the distribution of healthy samples x, from the external dataset D'. In details, The encoder
3

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maps the healthy samples x; to a latent space representation and the decoder reconstructs the data from the representation the generated data x/:
X = gen
Dgen (egen
(I)
The generator is trained minimize the reconstruction loss, ensuring that the generated data X closely approximates the original healthy samples x This enables the generator to capture the underlying distribution of healthy data, which is critical for subsequent feature augmentation and knowledge transfer.
3.1.2 Discriminator
In order maximally enhance the performance of generator, The discriminator Dacs used to distinguish between real healthy samples x from the external dataset D' and generated data x/. The output of Dass represents the probability that the sample from healthy subject. The details about the loss function can be seen in Appendix D
3.1.3 Inference on Internal Data
After pretraining, we use the Generator model to perform inference on all samples X, of D, and the reconstruction error& & is computed by:
E = MSE G (x.),
(2)
Since the generator is pretrained on healthy samples x/ from D', the reconstruction error & is smaller for healthy samples x; in the target dataset and larger for abnormal samples x,. Thus, the magnitude of & indirectly reflects the abnormality of the sample x;, i.e., the probability of disease. In design, we compute sequence-leve reconstruction error rather than point-wise error. This decision is motivated by the observation that point-wise errors are often highly sensitive local fluctuations and noise, which are common in medical time series due to physiological variability or sensor artifacts. In contrast, sequence-level feedback captures the global consistency and overall structural deviation from expected normal patterns, leading to more robust and stable anomaly signals. Additional details can be found in Appendix F
The reconstruction error is concatenated with the original features x; of the target dataset to form an augmented feature set:
X; concat &,,
The augmented dataset x; is used for subsequent model development.
3.2 Adaptive Contrastive Learner
Conventional contrastive learning relies on manually designed heuristics for constructing positive and negative pairs, which may introduce noise. To address this limitation, we propose an adaptive contrastive loss that leverages multi-head attention to guide the model learning which views should be pulled together or pushed apart. This approach eliminates the reliance on external labels and handcrafted pairing strategies, while producing robust and semantically rich representations. The Adaptive Contrastive Learner Ez, of our approach is designed with two components.
The first component a dilated convolutional network h(x),, which broadens the receptive field of the sequence. This network is crucial for capturing a broader range of information and enhancing the generalizability of the temporal representations. On the other hand, inspired from the local transformations to craft diverse data views in the latent space for contrastive learning Schneider et al. 2022). we recognized that MHA can significantly reduce the manual cost of designing positive and negative sample pairs from different perspectives of data. Consequently, we incorporated a second component into our encoder, which is our innovative learnable multi- views network g(x).
It introduces MHA to contrastive learning for extracting varying view representations adaptively. By leveraging both inter-wise and intra-wise contrastive losses, we ensure that the representations not only encompass diverse views but also exhibit distinct separability among different subjects within each view Figure 2 shown.

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Furthermore, to capture the intrinsic hierarchical information embedded in medical data, we adopt the concept from Wang al. [2023]
0000
View
Our contrastive learning considers subject, trial, epoch, and temporal, and optimizes the model using multiple InfoNCE losses Oord et al. (2018). Ultimately, we obtain representations that are beneficial for downstream medical time-series tasks.
View
3.2.1 Subject-wise Contrastive Loss Ls
Subject may exhibit unique physiological characteristics and pathological variations. Consequently, the subject-wiig wise contrastive learning is designed to enhance the discriminative representation of subject-speciffc features by increasing the similarity among samples from the same subject and the separability among samples from different subjects.
Figure 2: Mechanism of Multi-View Contrastive Learning
Specifically, let x,,, denote the i-th anchor sample in the given batch, which from subject The positive sample set S for X consists of all other samples xj.s with the same subject s+. Correspondingly, the negative sample set S includes all samples x,,=- from different subjects s-. Based this delineation of positive and negative samples, we define the subject-level contrastive loss function as follows:
Ls 3==1x
M -
log
exp (s(hi,,, h,,,,)) )/T)
(4)
Ex,,. __ e8; exp (s(h h,,, 1/T)
J.a Est
where j * and .),.)) denotes the cosine similarity between the respective representations. exp(.) the exponential function and M represents the batch size. h;,s corresponds the output of the network h(z) applied to the sample x, i.e., h h(x;,)) Similarly, h and hj are derived
from h(x (x),s+) and h(x,,--). The parameter 7 serves as a temperature parameter.
3.2.2 Trial-wise Contrastive Loss LR
Trials may demonstrate distinctive experimental conditions and variations in outcomes. To ad- dress this, trial-wise contrastive learning enhances trial-specific feature representation by promoting similarity within trials and separability between trials.
Concretely, assume Xi.r represent the i-th anchor sample in the batch, sourced from trial r. The set of positive samples R associated with x,,, encompasses all other samples xj,+ that from the same trial r+_ Conversely, the set of negative samples R, consists of all samples xj.r- originating from different trials Therefore, we establish the trial-wise contrastive loss function as below: exp (s(ha.,, hy )/r)
M
LR
Σ log
(5)
M
ER _x,,-- J..- ERT exp (s(ber (istsr.,g..)11) 1g/=)//)
where, ha, denotes the representation obtained by applying the network h(x) to the sample Xi,r. formally expressed as h = h(x,))). Consistent with this notation, hy. and h,, represent the feature representations resulting from the application of hxx) to the corresponding positive and negative samples x,r- and x
3.2.3 Epoch-wise Contrastive Loss LE
For epoch-wise contrastive learning, our hypothesis is that sample which undergoes minor modifica- tions, like temporal masking, should maintain a similar pattern compared to other sample. Hence, for each sample x; in batch, we employ data augmentation strategy that is random continuous masking. This strategy involves applying two independent augmentations to x,, resulting in augmented samples x! and x Since these augmented samples originate from the same anchor sample x,, they are encouraged to have high similarity between their feature representations and are considered as positive pair (x!, x2) In contrast, negative sample pairs are formed from augmented samples derived from different anchor samples, such as (x,, x3) and (x), x)). Finally, the epoch-level loss function is formulated as:
M log
exp hi hi
LE
Σ 1
(6)
M
Σ
M
exp [h: iit) + exp (b: (n:-ki))
1,
5

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The objective of this loss function is to ensure that two augmented samples originating from the same sample exhibit similar representations when observed at the same timestamp, while their representations differ when observed at other timestamps. Temporal-wise contrastive learning leverages the temporal consisteney within the data. In this context, let consider the anchor sample X of the i-th data point time & within a batch. By applying temporal masking, we generate two augmented samples as _t, and x2,-- The pair (x,,, xx,)) is classified positive sample pair because they are derived from the same anchor sample at timestamp t. Conversely, negative sample pairs are where hi! = h(x)) and h h(x)))
3.2.4 Temporal-wise Contrastive Loss Lr
defined as (x), ) and (x), x-)),, where / + 1_ These negative pairs are constructed from augmented samples originating from different temporal points, thereby encouraging the model to learn distinct representations for different time observations. The Temporal-wise contrastive loss is shown as:
M
LT
log Qi,
T
M
exp lii, hi.))
Q1.
(8)
T
(ii,, lil.-) - exp (lib.- ( bi?,,-)
Σ
exp
where fil, = (x),)) Similarly, analogous notations with are derived from the network f(x)).
3.2.5 Inter-view Contrastive Loss LIRV
The inter-view contrastive loss we propose aims to encourage model to capture informative features across different augmented views adaptively. Essentially, inter-view loss drives the representations from different views be distinguishable from each other, thereby promoting the diversity of views.
Let us consider batch of samples x;, where i = 1, M. For each sample x; apply data augmentation generate augmented samples, x and x2 These augmented samples subsequently fed into the MHA module, denoted as gxx). This module generates representations g.,. for each augmented branch E (1,2) and different views v, where g(x²') _ B(..)) The view representations from the given batch are then aggregated into g. {g),.. .... gMr.
Assume we have two views for each branch that V To facilitate inter-view contrastive learning, we define positive sample pairs as those from same view, such as (g), gi) and (#d.g3). Conversely, negative sample pairs are those from different views, such as (g) g1) and (g), gī). Hence, the inter-view contrastive loss is formulated as:
log exp(s(g), gF))
LIRV
(9)
1 exp(s(E) 83))
3.2.6 iitra-View Contrastive Loss LIAV
In addition inter-view loss, we also introduce the intra-view loss further refine the representations within the same view. This loss is designed to enhance discriminative power of representations by contrasting samples from the same view but different subjects, thus encouraging the model to capture more effectively.
view features
Similarly, assume we have subject-specific samples X,.s where i ranges from to M. For each of them, we apply data augmentation, resulting augmented samples x and x7. These augmented samples are then processed through MHA network g(x), which produces subject-specifie view representations & for each augmented branch & E {1,2) and various views i.e. gf... . g(x;,))
Suppose have V views and S subjects. To enable intra-view contrastive learning, we identify positive sample pairs as those from same view and subject. Therefore, we have positive pairs that are (g). +.t On other hand, The negative sample pairs are defined as the samples that differ subject the given view, which are (g),0.5 22

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-1 V
M
LIAV V Sastatess
Σ
log Ki
(10)
exp(s(B).e,) Bj,u (+/TT)
Kiv.
Σ
exp(s(Bi,n, B), -)/))
2.0 ES;
where 5 includes augmented sample set x sample x. under the same view v, Conversely, S consists of augmented samples that come from the same view but different subjects.
-. belong to same subject as given augmented
Thus, our view loss comprises both inter-view and intra-view contrastive losses:
Ly LIRV + LIAV
(11)
Finally, the overall contrastive loss function L is defined as:
C As Ls + As LR + Xg Le + Ar Lr + Av
(12)
where Äs AR A Ar Av = 1 2 in our practice. The details about loss weight sensitivity could be in Appendix D.2
3.3 Fine Tuninig
In order to use the Encader Fg of DAAC for downstream tasks, we utilize fine-tuning as follows:
it C(EL (x:)) C(h(x)),
(13)
where ý is the classification result for downstrcam medical discase diagnosis, and the training optimization objective is binary cross-entropy.
Experiments
This study utilized four target datasets corresponding to Alzheimer's 's disease, myncardial infarction, alcoholic liver disease and Parkinson's is discase. Additionally, four external datasets from independent institutions were incorporated facilitate
knowledge transfer. A detailed description of all datasets and the prepro- cessing protocols provided Appendix
datasets are publicly available and had received institutional review board (IRB) approval prior to their release.
All experiments were conducted on a workstation equipped with four NVIDIA RTX 4090 GPUs (24GB each). The models were implemented in PyTorch 1.13.1 with CUDA 11.6. All results were obtained under the same hardware configuration to ensure faimess and consistency in comparison.
4.1 Results on Partial Fine-tuning
To evaluate the representation quality of our proposed DAAC quickly, we adopt the Partial Fine-Tuning (PFT). PFT refers combining the Encoder with frozen parameters and trainable logistic regression linear classifier C. In this setup, utilizes 100% of the labeled data for training the supervised classification. The PFT results on the dataset are shown Table Our proposed DAAC model excels in metrics, including Accuracy, Precision, Recall, FI score, AUROC, and AUPRC, demonstrating the power of our representation learning.
Table 1: Partial Fine-tuning Results. ACL Adaptive Contrastive Learner; DE Discrepancy Estimator: COMET+ACL, COMET+DE represent ablations with only ACL or DE added to COMET; DAAC is the final model combining both ACL and DE. Bold indicates the best performance, and underline denotes the second best.
Models
Accuracy
Precision
Recall
Fi score
AUROC
AUPRC
TS2vee
66.48
5.33
67.72
5.09
67.40
5.20
66.32
5.46
74.12
1 6.85
72.96 7.21
TF-C
77.03
2.80
75.79
5.07
64.27
5.03
64.85
5.56
80.71
1 4.03
79.27 4.15
Mixing-up
16.16 1.38
52.62
4.90
50.81 = 1.32
37.37
1.98
64.42
6.49
62.85 = 6.07
TS-TCC
59.71 2..6
61.06
+ 8.63
60.33 3.26
8.39
67.53
10.04
68.33
SimCLR
57.16 2.05
36.67
3.91
2.21 53.57
49.11 + 4.26
56.67
3.91
52.10 1.41
CLOCS
66.99 2.84
67.17
2.96
67.33 2.99
66.91
+: 2.88
73.71
3.62
72.58 3.54
COMET
76.09
77.36
3.97
4.83
1.97
80.50
COMET+ACL
77.42 1.:22:00 1.40
78.64
3.73
76.11 4.98
76 26 5.26
82.41
5.19
81.30 5.56
COMET-DE
79.60 + 6.01
5.86 80.34
80.41
78. 50.50- 6.51
78. 75 6.46
86.32 4 6.54
85.85 + 6.99
5.97
7.17

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Braeen keeet
Mel
Selfesurperiieed
J
view representations comparison
subject-spoeiffe view representation
subjict-specifie view 2 representation
lnbel-gpeciffe view representation
lahel-speiffi view represcatation
loevo bace!
Supervied
(a)
(b)
(c)
Figure 3: Visualization of Multi- View Representations on the AD Dataset. The figure compares contrastive view representations obtained through self-supervised contrastive learning and 100% supervised fine-tuning.
4.2 Results on Full Fine-tuning
Unlike PFT, Full Fine-tuning (FFT) adapts all model parameters to the given target medical task, which means the Encoder and the classifier are trained together with supervision. FFT further enhances representation the downstream task. The results FFT the target dataset are exemplified using the AD, PD, PTB, MIMIC
dataset in Table and Table HI
4.3 Visualization of Views
To verify the learnable multi-view learning capability of ACL, we employed the UMAP Mcinnes et al. 2018 to reduce the dimensionality of view representations to two dimensions and visualize them shown in Fig. The visualizations provide strong validation from the following
UMAPMclnnes
aspects:
The Disparities between Diverse Views: that the designed inter-view contrastive loss enables the representations of view and view under different samples to show obvious disparities, effectively distinguishing the representations of each view.
By comparing the view representations as Fig. B (a), can be seen
The Intra-view Subject Discrepancies: The intra-view contrastive loss we propose fully considers the subject identity. setting the positive samples those samples with the same view and subject as the anchor sample. and the negative samples those with the same view but different subjects with the anchor sample. From the visualization results of patient-speeiffe view representations Fig. (b), it be found that there differences the representations of some diffcrent subjects under each view. In the self-supervised part, although label is not introduced, when observing the representations of different views with label coloring in Fig. (c), the representation differences between diseased subjects and normal subjects in the AD dataset can still be found. This indicates that the view representations have good generalization ability, which is beneficial for downstream disease diagnosis tasks.
addition, Fig. also shows that after adding 100% labeled data for supervised fine-tuning, the clustering of view representations is further enhanced. Overall, through this visualization, it is sufficient to prove the effectiveness of the adaptive contrastive learning.
Conclusion
this work, we tackle two fundamental challenges medical time-series diagnosis: the scarcity of labeled data and the limitations conventional contrastive learning that rely on handcrafted sample pairs. We propose DAAC, novel framework that enhances representation learning through a combination of external knowledge and adaptive contrastive strategies. By introducing Discrepancy Estimator trained on large-scale normal data, we effectively quantify and incorporate abnormality as auxiliary feature, reducing overfitting caused by limited target data. Additionally, our Adaptive Contrastive Learner, equipped with multi-ho head attention and hierarchical contrastive losses, enables the model to automatically discover meaningful relationships across diverse views and patient conditions without manual design. Together, these components allow DAAC to extract robust, generalizable features from complex medical time-series data. Experimental results four diagnostic

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Table 2: Full fine-tuning results of AD, TDBrain, PTB datasets. We use 100% and 10% of labeled data for fine-tuning Model names are consistent with Table and underline denotes the second best.
Bold indicates the best performance,
Datasets Fraction
Models
Accuracy
Precision
Recall
score
AUROC
AUPRC
TS2vee
S1.26
2
81.21
2.14
81.34 + 2,03
81.12
2,06
80,20
1.76
88.54
1.86
8.85
Mising-up
65.68 7.89
72.61
4.21
68.25 6.97
63.98
1.9.92
84.63
5.04
83.16
5.48
TS-TCC
73.55 10.00
71.22
6.13
73.83 9.65
71.86
11.59
+-5.1-1.1 5.11 86.17
85.73
5.11
100%
SimCLR
CLOCS
54.77
78.37 6.00
2.11 83.90
76.
7.03
75.78
7.93
91.17
2.51
90.72
3.05
COMET
81.50 1.46
88.31
82.95 5.39
83.33 + 5.15
94.44
2.37
94, 13
+ 2.06
COMET-DE
5.71
11.99
12.99
COMET+ACL
91.16 4.14
92.
3,66
90.50 4.51
00.88
4.33
2.76 98.03
06.22
06.03 2.82
DAAC
5.225
94.01
+: 3,98
92.71 + 5.86
92.97
5.65
1.71
98.03 1.75
TS2vee
73.28
74.
4.33
73.52
73.00
4.18
81.66
TF-C
75.96 11:21
70.48
11:48
75.58 20.588-211 11.59
75.38
11.47
81.38
14.19
81.56
13.68
Mixing-up
58.38 2: 3.33
61.85
4.38
61.94
3.42
58.17 4: 3.41
75.02 2: 6.14
73.44 1: 5,82
TS-TCC
77.83
79.73
7.49
84.12
7.32
84.12
SimCLR
56.09 = 2.25
53.81
5.74
51.73 2: 2.39
4.84 74.75
44.10
53.81 + 5.74
61.08 1.33
10%
CLOCS
76.97 + 3.01
81.70
74.69
3.26
86.91 3.61
56.70 3.64
COMET
91.43 3.12
92.52
2.36
90.71 3.56
91.14 3.31
2.84
96.
COMET-DE
93.47 3.52
91.54 2: 3.41
93.68 3.16
93. 13 3.50
98.27 1.34
98.30
1.34
COMET&ACL
92.77 2.35
92.92
2.43
92.55
2.39
92.66 2.37
97.30
97.39
DAAC
94.67 1.84
94.93
1.76
94.41
1.99
94.58 1.89
98.10
98.
TS2vee
80.21 1.08
$1.38
1.97
80.21
1.09
80.07 1.69
80.57 2.31
80.00 2.37
1.64
Mixing-up
$1.47 1.07
82.11
1.12
81.47
1.07
81.38
1.08
90..48 0.89
90..1 0.04
TS-TCC
77:42 2,80
77.68
2.93
77.42
2,86
77.37
2,86
87:37
3.06
87.61 3.22
SimCLR
100%
CLOCS
78.16 1.13
79.49
1.61
76.16
1.13
77.91
1.12
88.40 1.95
88.83
COMET
85.47 1.16
85.68
85.47
1.16
85.45
1,16
93.73
1.02
93.96
0.99
COM
90.61
90.56
1.40
90.16
1.94
91.55
2.38
95.09
2.07
96.07
COMET+ACL
93.26
2.33
92.81
1.76
93.44
1.66
93.07
+ 1.99
96.74 + 9.
977 60 88
TDBrain
DAAC
95.60 1:
94.95
1.69
95.73
1: 1.74
94,61
1.26
97.63
1.57
98,51 1.29
72.10
1.13
74:43
1.73
72:30
1.13
71.50
1.05
5000S
TF-C
59.14 1...::3.00 3.04
59.34
1: 3.19
39.14
3.04
58.98
2.94
59.56 1: 4.10
59.65
2.99
Mising-up
77.50 2.007
1.92
77.50
76.90
2,28
87.29
87.13
TS-TCC
71:23 1.57
0.66
71.23 1.57
09.18
2.25
80.21 - 2:21
10%
CLOCS
SimCLR
75.04 2.65
59.79 2.0.9
61.50
75.97
1,90
39.79 1:729:::20 2.09
75.04
2.65
39.06
74.83
2.82
2.86
84.25
60.50
3.27
1.90
55.96
84.37 3.52
1.41
COMET
79.28 4.64
79.83
4.83
79.25
4.64
79.19
4.62
88.39
4.13
88.38 3.96
COMET-DE
81.27 4.32
$1.90
4,22
82.30
4.33
82.07
+ 4.52
90122
4.02
90.50 4.09
COMET+ACL
82,20
3.91
3.60
DAAC
3.29 83,66
84.06
3,06
2.96 83,29
$4,60 1: 3.29
4.01 95.27
94.71 4.09
TSIve
$5.11= 1,00
2,21 31.82
70.81
3.99
79.00
3.63
M0.50
50.07 1.73
87,50 2.43
4.04
15.10
Mising-up
87,61 1:61:110 1.48
89.56
2,80
79. 79.30
1.67
82.56
2.00
89, 29 1.26
88. 94 1.01
TS-TCC
82.24 3.55
85.28
5.08
69.46
5.85
72.08
6.65
87.60 2.51
86, 26 3.00
100%
SimCLR
CLOCS
86.39 2.76
$4.19 1.32
87.06 2
85.85
1,80
77.95
73.89 2.95
1.79
80.71 2 1.78
2,80
90.11 2 2.07
85.85
70.70
87.35 3.36
COMET
$7.84
87.67
81.14 1. 3.68
83.45
3.22
92.95
1.56
87.47
COMET-DE
88.499 10-99:220 2.066
2.37
$3.19
1.59
85.67
1.07
93.58
1.94
58, 61 3.20
COMET+ACL
91.33 2: 2.03
90.89 1: 2.83
84.20
4.09
85.73
1: 3.62
94.16 1: 2.27
50.02 3.05
DAAC
2.35 93.68
93,28
85.39
3.84
3,27
1.57 95.56
90,23 28 3.49
TS2vee
$2.19 0.71
81.33
5.04
$3.35
0.99
80.18 2 1.09
1/2 0.97
TF-C
$5.37
1.23
$2.80 1: 2.35
79.94
0.71
$1.09
1.14
81.57
15.60
84.57 1: 12
Mising-up
TS-TCC
$3.38 1.53
2.11
72.24 2.45
75.27
2.64
$6.06
1.76
84.34 2.08
10%
CLOCS
SimCLR
$3.84 1034++20 2.15
87. 1.34
72.51
11661++4.000 4.063
4.77 75.44
87.19 + 1.34
00.50 3.84
COMET
$5.49
3.28
85.
2.60
81.65 6.00
84,01 5.61
94.83
1.08
92.45
2:22
COMET-DE
90.81
3.01
89.67 2,68
83.05 2.69
81.68
2.95
95.20
2.62
91.32 3.89
COMET+ACL
S9.67
5.62
DAAC
91.38
2-7.
91.56 3.12
83.23 2.65
83.61
3.56
96.30 + 3.22
97.87
4.02
tasks confirm that DAAC achieves state-of-the-ard performance, even with minimal labeled data, highlighting its potential for real-world deployment low-resource clinical settings.
While DAAC demonstrates its effectiveness, the current discrepancy estimation may still be suboptimal in fully leveraging external information. Although care is taken to prevent external data from overwhelming the target dataset, the representation of discrepancy remains somewhat coarse. Future work could explore more precise and adaptive strategies for modeling discrepancy, aiming to better balance external knowledge integration with the inherent distribution the target data. Additionally, we plan extend DAAC other modalities that share similar hierarchical or temporal structures, broadening its applicability. We also acknowledge that jointly optimizing all five loss weights may further improve performance, and we have included this direction a key part of our future work.

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Acknowledgments and Disclosure of Funding
Sincerely thank all the co-authors for their dedication and teamwork throughout this project. Together, brainstormed ideas, refined methods, and stayed up late preparing the paper and experiments. We especially grateful Ruiyuan Kang for his help in polishing the manuscript and offering valuable writing suggestions. We also thank Prof. Chenzhong Li Prof. Cheng Jiang for their continuous guidance and support.
We hope this research can contribute to the carly detection of neurodegenerative discases.
This work was supported by C10120250010 ("Early Warning and Acoustic Acoustic-Optical Electromagnetic Inter- ventions for Neurodegenerative Diseases Using Multimodal Diagnostic and Therapeutic Technologies"; "); UDF01002970 (University Development Fund Lund-Research Start-up Grant, Chenzhong Li); KQ002970 (Pengcheng Peacock Supporting Research Fund, Category A-LICHENZHONNO the Chengdu Science and Technology Program (NO. 2025- Y-O--FFF------ (188-00240-G33 GX); A10120250749 Precise Analysis of Blood Neuronal Exo- some Omics for Early Diagnosis of Alzheimer's Diseases; D10120250654 Shenzhen Longgang District Key Laboratory of Intelligent Biophotonics and Brain Disease Diagnosis and Treatment and the CAS President's International Fellowship Initiative (PIFI, No. 2025PVA0186).
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A Preliminary Knowledge on Medical Time Series
A.1 Hierarchical Structuring of Medical Time Series
To better understand the structure of medical time series data, this section provides a comprehensive explanation of its hierarchical organization, using EEG signals from Alzheimer's disease example. The dataset categorized into four hierarchical levels: Subject, Trial, Epoch, and Temporal, with each level representing a unique granularity of the data (Appendix figure(BI Unlike conventional time series, which typically consist of sample and observation levels, medical time series incorporate two additional layers: patient and trial. These extra layers enhance the dataset's s structure, enabling the development of methods tailored to the specific characteristics and analytical requirements of medical time series. As illustrated in Appendix Table[B]] many existing approaches utilize only a subset these levels, underscoring the importance of leveraging the complete hierarchy for more effective analysis.
Definition 1: Subject
A subject E R in medical time series data represents the collection of multiple trials corresponding to single individual. The subscript j denotes the subject ID. Trials associated with the same subject may differ due variations in data collection timeframes, sensor placement, patient conditions. A subject's data is generally represented as an aggregate of trials. Each trial within a subject P, is typically segmented into smaller samples to facilitate representation learning. To denote all the samples belonging to a specific subject Py, we use the notation Py.
Definition 2: Trial
A trial rs refers to a continuous set of observations collected over an extended time period (e.g.. 30 minutes). This can also be referred to a record. The subscript & denotes the trial ID. Trials are generally too lengthy (e-g., consisting of hundreds of thousands of observations) be directly processed by deep learning models. Therefore, they are usually divided into smaller subsequences, referred as samples or segments. The collection of all samples derived from a trial rs. is denoted by R..
Definition 3: Epoch
An epoch e. is sequence of consecutive observations derived from a trial, typically spanning a shorter time period (e.g., several seconds). In EEG (clectrenncepaalogaahh) an epoch refers to specific time window extracted from the continuous EEG signal, These time windows are used to isolate signal segments for further analysis, often aligned with specific events or experimental conditions. Fach epoch is composed of multiple observations measured at regular intervals over time range defined by M timestamps. To denote a specific epoch within a trial, we use Em, where wn represents the epoch index. The set of observations forming an epoch is
6m = {emmt t== 1, M..
Definition Temporal
temporal observation X.,, E R refers to a single data point or vector recorded at a specific timestamp t. The subscript n denotes the sample index, while represents the timestamp. For univariate time scrics, the temporal observation is a single real value, whereas, for multivariate time series, it is a vector with H dimensions. Temporal observations may represent physiological signals, laboratory measurements, other health-rclated indicators.
A.2 Data Scarcity and Heterogeneity for Medical Time Series
Data Scarcity Medical data, especially high-quality nourodegenerative discase data, often expensive, with high costs associated with data collection and annotation. Additionally, individual datasets typically have limited sample sizes and suffer from class imbalance issues (e.g., unequal ratio of healthy individuals to patients). These problems lead to overfitting during model training, poor generalization, and suboptimal classification performance.
Data Heterogeneity Medical data often exhibits multi multi-center characteristic, meaning datasets from different institutions show significant distributional differences. This heterogeneity makes it difficult to directly transfer models trained on one dataset to others. severely hindering the development and application of robust deep learning models.
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Table B1: Different Models Utilize Different Levels
Models
Subject
Trial
Epoch
Temporal
View
SimCLR
TF-C
Mixing-up
TNC
TS2vee
TS-TCC
CLOCS
COMET
DAAC
V
Subject p
Trial Tk
Epoch ém
Temporal Ema
Figure B1: Structure of Medical Time Series. The data organized into four levels: Subject (pz), Trial (rx), Epoch (cm), and Temporal (Cm.t), capturing different granularities of the data, from subject-level recordings to temporal observations within individual epochs.
Randen Contrncus Maserg
MHA
Classifier (finetune)
chamsot
Ext
Figure B2: Overview of DAAC
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B Supplementary Figures and Tables
C
Datasets and Data Preprocessing
the medical field, datasets from different hospitals often exhibit certain degree of distributional shift. Models trained on data from one hospital may demonstrate unstable performance when applied to others. To enhance model generalization, we propose leveraging DE to incorporate data from other hospitals. Although distributional differences exist, argue that the sequential patterns of normal data (i.e., data from healthy patients) can still provide valuable guidance for diagnosis the target hospital. In this study: The dataset from the current hospital is referred to as the internal dataset (or target dataset), which participates in the second and third stages of model training. Datasets from other hospitals, used for guidance, are termed external datasets which are from healthy patients and contribute the first stage of training.
C1 External Datasets
C1.1 Alzheimer's Disease Dataset
The AD dataset Miltiadous et al. 2023 is a publicly available EEG time-series dataset comprising four classes: 36 Alzheimer's discase (AD) patients, 23 frontotemporal dementia (FTD) patients, and 29 healthy control (HC) subjects. Recordings were ohtained using 19 channels a raw sampling rate of 500 Hz, with each subject contributing a single trial. The average trial durations were approximately 13.5 minutes for AD subjects (range:
5.1-21.3 minutes), 12 minutes for FTD subjects (range: 7.9-16.9 minutes), and 13.8 minutes for HC subjects (range: 12.5-16.5 minutes). A bandpass filter between 0,5-45 Hz was applied to all recordings. Subsequently, the data were downsampled to 256 Hz and segmented into non-overlapping 1-sccond windows (256 timestamps per segment), discarding any segments shorter than I second. Sixteen overlapping channels were selected from the original 19 channels for analysis. After preprocessing, the dataset contained total of 69,752 samples. We considered both subject-dependent and subject-independent evaluation setups: in the subject-dependent setup, 60%, 20%, and 20% of the samples were allocated to the training, validation, and test sets, respectively; in the subject-independeet setup, 60%, 20%, and 20% of the subjects (and their corresponding samples) were used for training, validation, and testing, respectively.
C1.2 Parkinson's Disease Dataset
The PD dataset used in this study consists of EEG recordings from 46 participants collected at the University of New Mexico (UNM, Albuquerque, New Mexico), as previously described in Annjum et al.] [2020]. The cohort includes 22 individuals diagnosed with Parkinson's 's disease (PD) in an Ofe-medication state and 24 healthy control (HCC subjects. EEG recordings were obtained using 64-channel Brain Vision system sampling rate of 500 Hz. For consistency across datasets, channels with matched naming conventions were selected for analysis. The EEG signals were high-pass filtered at I Hz to remove low ure-freuency noise, and no further preprocessing was applied. Only the first one minute of clesedlosed resting-state EEG data was used for each participant. Each 1-minute recording was segmented into non-overlapping 5-second windows, resulting in data represented as x 1250 where denotes the number of 5-second segments and 1250 is the number of timestamps per segment.
C1.3 Myocardial Infarction Dataset
The PTB-XL -L datasct Wagner et al. [2200] is a large-seale public ECG time-series datasct comprising recordings from 18,869 subjects across 12 channels, with five labels corresponding to four heart discase categories and one healthy control (HC) category. Each subject may contribute one more trials; however, to ensure consistency, subjects with varying diagnostic results across trials were excluded, resulting in final dataset of 17,596 subjects. Recordings were obtained as 10-second intervals and are available in two versions with sampling frequencies of 100 Hz and 500 Hz. In this study, we use the 500 Hz version. which is downsampled to 250 Hz and normalized using standard scalers. Each trial was then segmented into non-overlapping 1 1-second windows (250 timestamps per segment), discarding any segments shorter than I second. After preprocessing, the dataset contained 191,400 samples. subject-independent setup was adopted for dataset splitting, allocating 60%, 20%, and 20% of the subjects (and their corresponding samples) to the training, validation, and test sets, respectively.
C1.4 Alcoholic liver disease
This study was conducted using standard 12-lead surface ECG data Kim et al. [2017] collected from a tertiary teaching hospital in South Korea with 1,103 beds. The study protecol was approved by the Institutional Review Board of Ajou University Hospital. The database includes all ECGs recorded between June 1994, and July
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31, 2013. From this database, we randomly sampled 2.000 patients. For each patient, we extracted the first one-minute segment of ECG data and divided it into non-overlapping 5-second windows. This resulted in data tensors shape 12x11xx220 where n is number of 5-second segments, represents the standard 12-lead ECG channels, and 1250 is the number of temporal samples per segment.
C2 Target Datasets
C2.1 Alzheimer's 's Disease Dataset
The AD dataset [Escudero et al. 2006 consists of EEG recordings from 22 subjects, including 11 patients diagnosed with probable Alzheimer's 's disease (5 men, women; mean age 72.5 8.3 years) and healthy controls. Patients were recruited from the Alzheimer's ' Patients' Relatives Association of Valladolid (AFAVA) and underwent comprehensive clinical evaluations, including neurological and physical examinations, brain imaging. and Mini-Mental State Examination (MMSE) assessments. EEG recordings were conducted at the University Hospital of Valladolid (Spain) using Profile Study Room 2.3.411 EEG equipment (Oxford Instruments) with 19 electrode positions according to the international 10-20 system. Each subject contributed over 5 minutes of EEG data recorded in relaxed, eyes-closed, awake state to minimize artifacts. Signals were sampled at 256 Hz with 12-hit malog-to-diiina precision,Face Fach patient contributed an average of 30.0 + 12.5 trials, with each trial spanning 5-second interval across 16 channels. Prior to analysis, trials were standardized using a standard scaler and segmented into nine hall-overlapping 1-second samples, each containing 256 timestamps.
C2.2 Parkinson's Disease Dataset
The TDBrain dataset Van Dijk al. 2022 contains EEG recordings from 1,274 patients acquired across 33 channels 500 Hz during eyes-closed (EC) and eyes-open (EO) tasks, covering 60 distinct neurological conditions, with some patients diagnosed with multiple disorders. In this study, we focus on subset comprising 25 Parkinson's 's discase (PD) patients and 25 healthy controls, utilizing only the EC task for representation learning. Raw EC trials were preprocessed using a sliding window approach, where each trial was resampled to 256 Hz and divided into pseudo-trials of 2,560 timestamps (10 seconds). Each pseudo-trial was standardized and subsequently segmented into 19 half-overiapping I-second windows, each containing 256 timestamps. Each sample was assigned binary label (PD or healthy). ). along with patient and trial identifiers, where trial identifiers correspond to the generated patients (four PD and four healthy) allocated to the validation set, another eight patients to the test set, and the remaining subjects to the training set.
souuo--rrals.A patient-inde independent split was employed, with samples from eight
C2.3 Myocardial Infarction Dataset
The PTB dataset Bousseljot al. [1955] contains ECG recordings from 290 patients across 15 channels, sampled 1,000 Hz, and covers eight types of heart diseases. In this study, focus on a subset comprising 198 patients for binary classification task, specifically distinguishing between myocardial infarction (MI) and healthy controls. The ECG signals were resampled to 250 Hz, normalized using a standard scaler, and segmented into individual heartbeats to preserve critical peak information. as standard sliding window approach may lead to information Ths segmentation process involved several steps: (1) determining the maximum heartbeat duration by calculating the median R-peak interval across all channels in each trial, excluding outliers; (2) identifying the position of the first R-peak using the median value across channels; (3) splitting raw trials into individual heartbeats based on the median R-peak interval, with segments extended symmetrically around the R-peak; and (4) applying zero- padding to ensure uniform sample lengths.A patient-independent split was adopted, with samples from 28 patients (7 healthy and 21 MI) allocated to the validation and test sets, and the remaining subjects used for training.
C2.4 Alcoholic liver disease
2023 which contains multi-source electrocardiogram (ECG) recordings from ICU and emergency department patients Beth Isracl Deaconess Medical Center, along with matched ICD-10 diagnosis codes. From this dataset, we sampled 200 patients for model development. For each subject, we extract the first one-minute segment of ECG data and divide it into non-overlapping 5-second windows. This results in input tensors of shape x 1250 where denotes the number of segments, 12 corresponds the standard 12-lead ECG channels, and 1250 is the number of temporal samples per segment. To standardize diagnosis codes, we remove decimal points, trailing alphabetic suffixes (c.g., A/B), and padding characters such as trailing "X". We apply hierarchical label propagation to map fine- mne-grained codes to higher- level diagnoses (e.g., K7030 + K703 + K70). To ensure statistical robustness, we retain only those labels that appear at least six times in both the internal validation set (fold 18) and the test set (fold 19). Finally, the task is
We utilize the MIMIC-I-ECG dataset Gow al.
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Tormulated as binary classification problem: samples labeled with K70 are assigned a label of (indicating the presence of alcoholic liver disease), while all other samples assigned label of 0 (indicating absence).
C3 Domain Shift Study
A Although we hope that the external normal data assumes a certain level of domain alignment. In practice, however, external and target datasets exhibit notable differences, including variations in population demographics and acquisition protocols.
For example, in the AD task. the external dataset [Miltiadous et al. 2023 has average subject age of 63.6 years, while target dataset [Escudero et al. 20066) ) has an average age of 72.5 years. When training Discrepancy Estimator (DE) using this external dataset, we indeed observe signs of negative transfer--secificcllly in Table 2, the AUROC of COMET+DE drops from 94,44 to 93.97.
Despite this, DAAC still outperforms both COMET+ACL and COMET+DE in the same setting, as shown in Table 2. This suggests that the Adaptive Contrastive Learnrr (ACL) effectively mitigates the potential negative impact introduced by domain-shifted external data, and in some cases, even corrects for suboptimal guidance from the DE, This robustness highlights the strength DAAC's multi-stage design handling real-world domain variability.
D
Loss Setups
D.1
Loss Funtion DE
The model is trained by optimizing two loss functions: the discriminator loss and the generator loss. During the training process, the discriminator is first updated using the discriminator loss (Lp), followed by the generator. The two components updated alternately.
(1) Discriminator Loss:
Ln - + L.uss)
where T.D.y = BCF(C( 1) indicates the loss for real data, encouraging the discriminator to classify real data as 1. I.ues BCF(C((ann) () represents the loss for generated data, encouraging the discriminator to classify generated data O. BCE is the binary cross-entropy loss function.
(2) Generator Loss:
LG LG + LGaon
where LGG,, BCE(C(dZ);)) indicates The adversarial loss, encouraging the discriminator classify generated data as T.Guoom MSF(21;, xx.;) represents the reconstruction loss, ensuring that the generated data afr; is close to the original data 2æ; MSE is the mean squared error loss function.
D.2 Loss Weight Sensitivity Analysis
To evaluate the rohustness and effectiveness of our loss function design, we performed comprehensive sensitivity analysis on the weighting strategy of five components. The total loss a weighted sum four hierarchical contrastive losses and one view contrastive loss (Lv). In our main experiments, we used a fixed configuration of 1:1:1:1:2, which we found to yield consistent robust performance across datasets.
The first four hierarchical components were assigned equal weights (1:1:1:1), following prior practices (Wang) al. (2023) ). which we hypothesized to provide strong default setting. To test this assumption. individually perturbed each of these four weights while keeping the others fixed, and evaluated the model on datasets (AD, PTB, TDBrain, MIMIC) under both 100% and 10% data regimes. Additionally, to investigate the contribution of the proposed ACL, we varied the weight of Lv from 0 to 3 and monitored the impact on overall performance.
The full results are provided in Table DI We summarize the main findings as follows:
(1) Effectiveness of Lv: Removing Lv (i.c., setting its weight to 0) consistently led to substantial performance degradation across all evaluation metrics and datasets, highlighting its critical role in enhancing cross-view representation alignment.
(2) Performance saturation with increasing Lv: Increasing the weight of Ly from to led noticeable gains accuracy, Fl score, AUROC, and AUPRC. However, further increasing weight resulted only marginal or dataset-speciffc improvements, suggesting a saturation effect.
(3) Default weight robustness: Perturbing the weights of the first four components (e.g.. 1:1:2:1:1, 1:2:1:1:1) produced only minor variations in performance, indicating that the default configuration (1:1:1:1) is generally robust and effective setting.
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These findings provide empirical justification for our selected weight configuration and demonstrate that our method performs reliably without the need for extensive tuning. Further improvements may be achievable through joint or dynamic weight optimization, which we leave as future work.
E
Ablation Study
E.1
Mutual Information Analysis of Diserepancy Estimator Reconstruction Error
We evaluate the statistical relevance of the Discrepancy Estimator (DE) derived reconstruction error by computing mutual information (MI) scores between each feature and the disease label on the AD dataset. The MI score quantifies the amount shared information between feature and the classification target, with higher values indicating greater discriminative power. As summarized in Table Hi the reconstruction error achieves the highest MI score (0.0295), substantially outperforming all original features (maximum MI among them: 0.0159). These results demonstrate that the reconstruction error serves a highly informative feature, supporting its design as proxy for disease probability within the DAAC framework.
E.2 Ablation study of contrastive blocks
We conduct ablation experiments evaluate the contribution of each contrastive loss branch in ACL. As shown Table full model (P+R+S+O+V) achieves best performance. and each added block improves results incrementally. Notably, adding the view loss (Iv) leads to a 3. 1% improvement on average. We adopt a fixed 1:1:1:1:2 weighting scheme for the five loss components, where Ly includes both inter- and intra-view terms. We did not tune these weights, specific dataset. Despite tuning, the model achieves SOTA results (see Table I and Table 2).
goal was to verify the method's generality rather than overfit to
E.3 Ablation Study on Ratio of External Datasets
To investigate the impact of the normal population size used during pre-training, conduct systematic ablation experiments on four datasets: AD, PTB, TDBrain and MIMIC. Specifically, we vary amount of normal samples used the pre-training stage across four proportions: 5%, 25%, 50%, and 100%, as shown in Table For cach setting, we evaluate the model under four levels of labeled training data (5%, 25%, 50%, 100%) in the downstream task.
We summarize three key findings from the ablation study ratio of external datasets:
(I) Across all datasets and downstream data scales, increasing the normal population size consistently improves model performance;
(2) The performance gain existed under low-resource (e.g.. 5%) labeled settings, highlighting the effectiveness leveraging normal population pre-training to reduce annotation dependency;
(3) observe a stable and monotonic improvement trend without performance degradation, indicating the robustness and scalability of our method.
These findings support the practicality our approach in real-world scenarios where the collection of full-scale healthy cohort might be challenging. Even subset of normal population data can yield substantial performance improvements, making pre-training strategy feasible and effective for deployment.
F Diserepancy Estimator Study
F.1
DE Further Study
In our shree-stage DAAC framework, the Discrepancy Estimator (DE) serves as the initial-stage module for modeling the abnormality of unlabeled target data, using external normal data as a reference. A key design choice is how to represent the discrepancy feature that serves as input to downstream stages.
We adopt sequence-levé scalar reconstruction error, computed as the mean squared error (MSE) between an input sequence and its reconstruction via autoencoder trained only on external normal data. This design intended to provide a robust, task-agnostic anomaly signal, especially under distribution shift.
evaluate this design decision, we conduct an ablation study comparing our default sequence-level scalar with several finer aherneg alternatives:
As shown in the table, while channel-wiss wise MSE vector and point-wis wise error sequence alternatives preserve more local detail, and the cluster-wist distance reflects latent deviation from normal patterns, their performance is consistently inferior to scalar design. This degradation can be attributed to: (1) Sensitivity to noise in
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Table DI: Comparison of performance metries under different hyperparameter configurations on AD, PTB, TDBrain and MIMIC datasets.
Danset
Conti
Accunacy
Prectsion
scoce
AUROC
AUPRC
1,1.1.10
84.50::4446
98.31
82.95
83.35:5515
93.0022.37
94.10#2.48
68.50
90.20:23000
87. 4.:0::::0
58.10:24000
95.50:2.10
95:60:2.00
1,111.12
4:116:4444
32.10.3.66
4..50:4.11
90.88:44.33
26.00:2.66
26.30:12883
1,11111
91:001155
59.06:5.544
5.15
90.961999
96 05.44:22.88
11,12,
8,.20::4330
$9.30:54..00
86, 06.00344.60
$7.30:4..0
94.05=22.00
94.75
1,1.1.11
87.402:4.10
89.50:34
86 16.22-44550
87.40144.00 20
80122.85
94.0012.95
12111
86900=4.00
$9.20:54.00
96.50:44:00
87.10:44.50
94,40=2.50
94.50+22.00
2.J.U.1
86.00=4470
58.70:44.0
06:20::::10
$6:80:54.00
03,90=3.00
94.00+3.000
AD
91,43::3122
92.52.1.36
90.71.
91.1413331
92,201.2.84
22.30:1..22
22:90:22.00
92.80+22.00 10
92. 10:22.50
92.20:42:45
96.70:11000
96 10:401115
1.11111
92.77
92.92:12.43
92. 42.55-22..99
92.66.12.37
97.40:11.00
97.90-1144
10
1.1.1.1
02.46:2.44
90.922361
92.63:2.01
91 31.54:23.00
96.84:1 1.89
97.6421 1.33
11.12.
92.00:2.800
92.50:2..50
91,9082..70
92.10:2:.00
25,20=:2.00
95.10:2200 05
1121.
91,90:2.00
92,40-42.55
91. 21,0022.55
92.00:2:000
94,50=2230
94,40:22005
91,001::000
92.10:12.00
9), 20.701
91 71.9012.70
26.001:2.00
94.1011215
2.1.T.1
31.00:3.00
92.00:::000
01.50422.00
91:80+275
03.40=2.50
58.50+2225
86.36611444
57.1811128
77.87.22.55
50.7912.44
92.6012.35
19.0911644
80:70:11500
19.60:22100 10
83. 3.000
$580:2.90
94:00:22100
90.10+240
1.1112
91.33-2200
90.59:22.33
84.20:1:.09
87.6413822
94.16::2337
90,02:43.05
1.11133
91.46-1.90
59.36-62.43
83. 63.56-333.88
$7.38:4.10
94.06-22.43
90.11:2711
G.1.1.1
NV 20.00:2.000
89. 19.30:22.00 30
R2, 1:2:2.00
85 35.00:0000
95 20000:2.00
89 19.0012 30
1121133
$8.60:2.10
$8.90:42.50
31,70:33.10
$4.0033.00
93:45=2.155
89.70=2244
88. 2.00
$0.30.1..00
30.30
$4.1013..0
25.20
1M.0012.35
2..00:2.30
$7.90:22.00
90:70:33:40
$3,0063.30
02:90=2.50
19 40.00-33300
PTB
90.32.11.61
99.67111.94
85.85:12.99
87.35:12.36
91,30.:1.11
90.46.1..99
L.L.I
20000:2000
$9.30:13.20
34.001:3:00
20.30:22.00
14:10:22300
92.20:3.100
1.111
89.0722.9
10 19:4622:477
83. 1:2
85.353381
94/422321
L.1.1.
89.77::2.22
88.9783.01
84.04-3.42
86.15:44:22
95.77=44.11
95.01+3..00
10
90000: 1900
19,0012.10 10
$4.001:3.10
36.002300
9540012.50
91 50.50.0000
1121.
20.50=22100
89.60+42.00
84.00:33.20
85,00+3.00
92,90=2.60
91.70+2800
2.00
39.50.22.00
83, 90..3.444
35.00.3.00
22.5012200
91.0011 70
ZIELT
2.300
89.30122000
33.5013350
85.1013.40
91,80
91.1011:80
GEEDD
1514
15.6811.50
1544711116
85.451116
91.0021.00 1.00
99.062000
LILL1
91,20::1.90
90.60:11.80
90.90::..75
91.101185
95.00::1.00
96.20:11655
1.112
03.6622.33
92.8111.6
93.411:1.22
93074192
96.94:1.98
97.001118 1.82
1.1.113
93.46:2.33
92.49:22007
03.06::334
93,49-213
96.39:1.56
12.4++1338
90,30:11M5
100011100
90.00:1190 1.0
94, 1.79
95
95.0011 1.70
L121.
90.001200
10.0011235
90.30.11995
94,10
95.2011175
1,111
80
20.00:2.00
89.7002:.15
80
90:02.000
90,009:100
03,50
94.50+1.8 1,00
29.40
39.20122225
29.5
39.80.12.00
12,40.1550
14.0011885
TDSman
79:288:4.84
798824483
79.19:44:
W2:419
18 95
L.LLL
81.00:3.600
80.80:13.00
80.90:13.770
$1,0013.65
92.60833.50
93.80:33.00
1.112
81.20:3348
81,8313911
81.00:2..51
$2.091300
9273
94.11144499
1.113
$1.49-1.92
$1.0783211
31.274374
94.01 2:211 94
11.113
8..70:3.500
$0.50-33.00
80. 30.80:033.70
$0.90:53.00
91.60:33.60
92.50+
11221
30.00
$0.20-13.000
30.00113.000
30.00.13.00
91,00.:3.70
92.4011...00
12111
800 4.:00000
$0.00:33000
80. 30.00:::.000
$0.30::3000
30440::350
91.50+53 T0
2.J.I.
79.0014.20
79.90.4.000
79:70-44.00
79:80:24.10
89.00
31.2011..00
80.551185 855
78.0011170
79.6821 1.80
90:1 1.55
14.101
$2.75
19:911335
$1.002119
88.402:
1.111
21,30:22.3
82.89:223
80.10::4.09
32.73-3322
90.66::2.27
58.02+3.055
1.113
82.91 11.00
12.10:199
96.7411.88
18.1111885
11211
80.05
$1.30:11885
75,50::150
$0.50:1185
89.10=1..0
$6.00 1.60
20.0
$1.45.1199
19.1511355
10.7511900
19.30.1177
16.0011 666
70.70-22.00
11.1011000
75,
18.50:1885
10.011877
88.90=1.700 1.70
85.50+1.65 1.65
70:25::2.10
$0.40::2000
77:05::995
79.05 90
87.8021170 1.76
85 10:00:1:11 70
MIMIC
75.9024.00
77.35::4.05
78.30:33.55
85,90:3.50
37.00:4:3885
79:9523370
76.002:3900
19.40:23995
1.111.
80.10:2293
$1.21
78.95=2..9
81.35:33.81
8999::3.67
35.82=3.71
1.113
79:85
30.9513366
78.99113.88
$1.51.1.399
901.3.000
881 15.35
10
1.121.1
75:00115:800
30.25:13.00
77.10:1..00
78.65.1395
88. 10..300
86 36.00-33.60
1.111.11
70, 30:::333
50. 50.35
77.65::3.85
78.90:23.00 3.00
89. 3.55
96 $6.50:53.55
1.2.1.13
78.05 4-400
79.80-23.85
77:001-4.10
78.40:34.00
87.900333500
$6.1011...00
2.1.1.111
76.203-4.30
78.95:54.00 (000
766 91.00:2:400 000
77.90:5411 10
87. 9:::00000
8S 35.90:55.00
26

---

Table E1: Mutual information scores between features and disease labels the AD dataset. The Discrepancy Estimator reconstruction loss achieves the highest mutual information, Bold indicates the best performance.
Feature
Mulual Information Score
Reconstruction
0.0295
Feature
0.01559
Feature
0.0140
Feature
0,0064
Feature 15
0.0054
Feature
0.0053
Feature?
0.0045
Feature
Feature II
0.0028
Feature
0.0023
Feature 12
0.0013
Feature
0.0001
Feature
Feature
0,0000
Feature
Feature 14
0,0000
external data; (2) Overfitting risks due to fine- ene-grained alignment across domains with potential distribution shifts (e.g., different acquisition hardware or subject demographics).
contrast, the scalar discrepancy offers a stable and compact abnormality signal, less vulnerable to non- transferable patterns. Importantly, we emphasize that this discrepancy is only used during the first stage of DAAC, and is subsequently complemented and refined by the multi-level Adaptive Contrastive Learner in Stage which captures high-resolution temporal and spatial semantics.
This study demonstrates that our scalar discrepancy signal provides effective balance between robustness informativeness, supporting more generalizable downstream representation learning diverse medical time-series applications.
F.2
DE Assumption Validation
We executed KDE analysis to empirically validate the assumption. As shown in Figure FT in the Appendix, normal samples show tightly clustered reconstruction errors around low values, while abnormal samples follow positively skewed distribution, with longer tail toward higher errors. This confirms that our model tends to assign higher reconstruction errors to abnormal samples. Additionally, using reconstruction score yields an AUROC 0.974 on the AD dataset, further supporting its discriminative effectiveness.
anomaly
KDE of Reconstruction Error (Skewed Abnormal Distribution)
Nommal
(shunes)
Dengty
1
Reconstruction Error
Figure F1: DE Assumption Validation
G Training Process Details
G.1 Training and Resource Requirements
We conducted all training experiments using an NVIDIA RTX 4090 GPU. The training and deployment efficiency of model different datasets is summarized as follows:
27

---

Table E2: Ablation study of contrastive blocks on the AD, PTB, TDBrain and MIMIC dataset under 100% and 10% label settings. O: baseline, S: subject, R: temporal, P: epoch, V: view. Bold indicates the best performance, and underline denotes the second best.
Dataset
Fraction
Blocks
Accuracy
Precision
Recall
F1 score
AUROC
AUPRC
O
$1.80 + 10.71
8i.25 28
13.13
79.66
12.07
78.30)
13.78
92.54 + 4.18
92.36 + 4.34
2.89
81
1.68
3.56
3.47
91.01
1.96
90.81
R
78.58
14.99
83.14
11.66
76.31
16.66
74.53
18.69
85.05
12.74
84.44 1 13.22
100%
P
12.69
13.13
78.67
11.81
60.74
14.50
07.04
17.27
83.30
13.91
82.57
14.29
S+O
86
1.63
92:21 +: 1.40
RiS+O
85.57 + 4.04
88
2.58
84.31
4.79
84.73 + 4.50
93.28
+ 2.52
92.08 + 2.83
P+R+S+O
81.50 = 1.46
88
2.42 92.10
82,
5.39
83.33 = 5.15
91.44
+ 2.37
93 + 18
P+R-S+O+Y
4.51
90.88
4.33
2.76
2.82
AD
0
80.35 0:25
87.00 9.14
S7.00 2.04
85.35
1: 5.32
92.02 1: 5.50
32.77 1: 8.72
77 30 + 3.63
78
3.75
78.
3.75
76.
3.91
85.43 + 3.61
84.63 +: 3.76
77.83
$3.45
75.41
14.35
75.41 19.62
83.27
83.27 17.00
10%
P
71 15.31
76.91
15. 15.56
68, 7 16.46
92.52 + 2.36
97.61 +: 16.98
76.06 +: 16. 19
S+O
62.73 4 2.05
84
2.05
84.33
2.71
81.51 1: 2.07
90.02 1: 2.47
90.02 1: 2
R+S+O
91. 01.74-
3.01
90. 00
90.70 3.34
95.86 2.63
95.86 2.67
P+R+S+O
91 43 + 3.12
02 52
2.36
90. 71
3.56
91. 3.56
96.44
+ 2.84
96 48 +: 2
S+R+SHO+V
92.77
2.35
92.92
2.43
92.55
2.39
92,66
2.397
97.30 1.40
97.39 1.40
0
86.27 1:73
81.21 1.25
71:74
1.01
79.78 = 3.37
85.
2.25
84.70 2.14
S
84:30
2.56
84.00
2.16
75.85
5.60
71.74 + 5.13
88.06 + 2.05
84.80 + 2.15
88
88.
82.
81.72
89.22 3.96
86.21
100%
P
88.85
3.22
88.74
2.30
82.78
6.11
84.75 d: 5.38
94.10 1: 1.81
89.61 1: 2.81
S+O
84.38
2.34
84.13
1.18
75.49
4.65
77.00 + 4.76
90.37 + 2.50
86.57 + 4.32
R+S+O
77.54
4.58
3.94
1.85
1.96
PcR+S4O
86.36-
1.44
88.18-
1.28
77.87
2.55
80.79
2.44
93.44 + 2.35
80.10) + 1.64
P+R-S+O+V
91.33 2.03
90.89
2.83
84.20
4.09
85.73
3.82
94.16 + 2:27
90.02 + 3
PTB
0
56.75
52.45
90.71
3.50
86:34-1 0.73
84.75-
1.49
80.21
1.78
81.90
1,25
88.36 + 1.31
85.20 + 1.17
R
88.1
1.75
86.36
2:28
84.16
3.55
82, 16 3.55
91.80 + 2.50
90.45 + 1.50
10%
$9.52
2.23
88.81
2.71
81.60
4.36
84.27
3.45
93.06
86
91.83
S+0
85.81 = 1.74
81.1 0.76
80.07=
5.18
81
3,82
90.22
+ 75
87.57 4 3.12
R+S+O
85.88 3.08
83.60
3.61
82.45
1.94
82.45
1.94
92.79
1.79
88.68 1.91
P+R+S=O
88.32
88 67
82.85
2.90
84.35
92.78
90.46
3.00
P+R+S+O+V
89.67 : 2.93
89.66
2.47
83.36
3.62
85.35
= 3.81
95.35 + 3.67
94.94 + 3.71
89 2.15
90.50
2.07
80.92
2.15
80.80
2.17
98..79
96.84
S
87.86 1: 2.85
78.98 2.51
77.88
2.85
77.63 1: 3.01
88.44 1: 2.23
88.96
2.14
R
91.44 + 1.79
90
1.75
92.44
1.79
92.34 + 1.80
95,200 + 1.29
9T.31 + 1.46
100%
3. 70
91.27
3.70
91.
3.71
91.59
2.01
S+O
80.38 1: 4.22
81.59
3.70
80.35
4.22
80. 1: 4.38
91.35 1. 2.24
91.64 1. 2.05
RiS+O
86.01
4.29
86.35
3.81
86.01
4.20
85.95 + 4.36
94.14 + 2.77
94.37 + 2.58
P+R+S+O
85.47
85.45
93.75 4:
93.96 0.90
PoR+S+O4Y
93.26
2.33
92.84
1.76
93.44
1,62
93.07
1.92
96.74 1.98
97.60 + 1.82
TDBrain
0
81 =: 1.38
01
3.97
81 = 1.38
81.25 = 2.18
98.188 3 3.52
93..21 1 3.53
74.
2.01
2.09
79.86
2.17
81.92
81.76
R
81.96- 8.22
81.43
9.20
81.76
8.22
81.96 + 8.23
93.54 + 5.80
94.73 + 6.08
10%
$1,38 14.69
80.58
14.72
$1.38
14.69
$1.36
14.93
93,23
11.81
93.52
11.10
S&O
76.60
3.07
74.92
2.57
74.34 2.55
84.51
3.07
84.40 3.07
R+S-O
81 = 1.74
80.55
1.02
81. 90
4.74
81.61 - 4.99
91.21 + 3.52
90.73 + 3.57
P+R+SHO
79.28 4.64
79.83
4.83
79.25
4.64
79.19
1: 4.62
88.39
1: 4.13
88.38 4.00
POR+SHO+Y
3.91
81.90
3..1
82.09
3.60
93.73
3.27
94.81 3.49
18.64 2.35
19:12
2.22
78.64
2.355
78.39
3: 2.31
88.41
1: 2
88. 2.33
2.67
85.27 2.70
R
79.75 + 2.01
80.01
2.05
80.23
2.00
80.12
2.03
80.10 4: 1.85
80.73 4: 1.77
100%
S+O
79. + 3.12
T1.11
75.01
80.38
2.60
71.11
79.32
3.12
79.55 3.18
81.56
80.00 + 2.71
84.79
80.08 + 2.65
R+S+O
77 77.63-2
2.73
78
2.52
77.63
2.75
77.57 1: 2.79
87.70 4: 2.21
87.91 1: 2.18
PoR
79
2.45
80.18
2.60
79.00
3.01
81.02 + 2.75
89.73 + 2.18
80.61 + 2.42
P+R+S+O+V
82.73
90.66 2.27
3,05
MIMIC
0
74.08 3.45
74.22-
3.51
74.08
3.45
74.03 + 3.50
86. + 3.24
86. 040.5411 + 3.20
S
70.93
2.4T
71.50
2.39
70.93
2.47
70.77 - 2.58
81,82 + 2.91
81.60 1 2.88
R
78 3.02
77.24
76.51
76.53
3.11
87.45
87.81
10%
S+O
71.14 2.75
75.30 3.34
72.20
76.70-
2.61
3,62
71.14
75.89
2.75
3.84
70.90
75.98
2.79
3.92
83.18 1: 2.53
87.33 + 3.00
87.541 2.55
83.02 1: 2.97
R4S+0
74.98
3.28
75.89-
3.05
74.98
3.28
74.77
+ 3.34
85.70+ 2.65
85.93-
P+R+S+0
77 77.61 = 3.12
78. 39
= 3,26
27.
23
3.51
78.07
+ 3.31
83.
2: 3.03
87,
20 3.14
P+R+S+O+V
80,10 1: 2.93
81.21 2.88
78.95
2.89
81.35
3.81
89.90 1. 3.67
88.82 3.71
(1) AD dataset: Stage training took 1.2 hours, Stage took 3.2 hours, and Stage took 0.2 hours. The deployed model for inference requires only 4 GB of GPU memory.
(2) PDB datasct: Stage training took 2 hours, Stage took 6.5 hours, and Stage 3 took 1.8 hours. The deployed model for inference requires only 6.2 GB GB of GPU memory.
(3) TDBrain dataset: Stage training took 1.8 hours. Stage 2 took 5.5 hours, and Stage 3 took 0.5 hours. The deployed model for inference requires only 5.3 GB of GPU memory.
(4) MIMIC dataset: Stage I training took 2.4 hours. Stage 2 took 6.1 hours, and Stage 3 took 0.9 hours. The deployed model for inference requires only 5.6 GB of GPU memory.
28

---

Table E3: Comparison of model performance on AD, PTB, TDBrain and MIMIC datasets under different ratios of external data.
Dutuset
Label Praction (%)
External Ratio
Accuracy
Precesion
Recall
Pl score
AUROC
AUPRC
100%
93.23 5.25
94.01
3,98
92.71 5.86
92,07
5,65
1.71
98031
100
50%
92.21 3.89
93.48 + 3.94
92.22 2:444400 4.61
92,70 = 4.13
97.10 + 3.21
97.96 3.28
25%
91.45 4.21
93.10 1: 4.55
91.73 1: 4.33
92.04 1: 4.14
96.84 +: 3.95
96.67 4.22
91.13
92.154: 3.06
90.70 4.01
91.88 4.33
96.341 2:22
96.
2.23
100%
94,07 1.84
94.93 + 1.76
94.41 + 1.99
94,58 1.89
98.30 + 1.20
98.19 #:1.15
50%
93.24 2.86
94.01 +: 2,91
93.76 +: 2.63
93.22 =: 2.67
98.01 + 1.72
97.94 1.88
25%
92.93
3.25
3.60
93.07
5%
92.87
2.35
93.02 2.43
92.80 + 2.37
93.42 2.87
97.50 + 1:45
97. 1.57
100%
93.65
2.35
93.28 + 1.56
85.39 +:: 3.84
87.64 3.27
95.56 4: 1.57
99.28 =:: 3.49
50%
92.60
92.89 2:63
85,
85.71
3,44
95.30 4:
90:00:1
25%
92,12 2: 3.15
92.55 + 2:00
84.54 5.19
85 69: 4.31
94.27 4: 2:83
90 16:1 3.98
5%
91,50 2,03
91.89 1: 2.83
$4.40:1 4.09
$5.67 d: 3.71
94:23 4: 1.94
90.05 3.20
PTB
91.35.1
91.5611
2.65
85.61 3,56
97.87:1
50%
91.08 3.10
91.19 + 2:96
$4.91 +: 3.92
85.55
3,62
96.36 +: 3.42
96.88 3.55
25%
90,93 3.48
90.61 +: 3:27
$4.52 + 4.34
85.48 di 3.89
96,07 +: 3.88
96.03 4.12
90. 80.80:2:
1:2.47
80:80: 4.86
85.43 33227
3.51
95 80:80:1:
100%
95.60 1.20
94.95 + 1.69
95.73 +: 1.74
94.61 1.26
97.63 +: 1,57
98.51 1.29
100
50%6
95.45 d: 2,49
94.03 +: 2.12
94.88 d:: 2.38
94.30 db 2.28
97.21 +: 2.35
98.21 2.22
2.5
94.22
93.64 2:44
93.31 2.73
5%
93.51 3.05
93.05 3.11
93.55 :t: 3.28
93.20s:: 3,16
96.58 +: 3:33
97.77 3.47
TDBrain
100%
83.66
3.29
84.06 4:3.6
83.29 +:: 2.96
84.60 +:3.29
95,27 +: 4.01
94.71-14.009
3.77
94.00
25%
82.83 =:: 4.01
83.35 +: 4.39
82.55 4.337
83.50 4.22
94.12 :: 4.20
93.43 4.45
5%6
82.35:2 4.65
82.22 d: 4.51
82.03 +: 4.60
82.66 di 4.68
94,07 4.82
93.04 4.77
100%
1,56
84.64 3.27
91.56
88.98
100
50%
82.35 =: 2,46
83.76 + 1.99
81. 06.06.22 =: 3.58
83.94 3.64
91.30 :: 2:44
58. 73 3.14
25%
81.88
1.98
83.57 4: 1.67
50.83 ::: 3.63
83.61 2.98
90.92 4: 3.11
$8.34 2.95
81.51
1.66
3.22
82.94
3,05
2.13
MIMIC:
100%
$1.50 2.77
82,66 1: 3.12
80.23 :1: 2.65
82.61 :: 3,56
90,30 :: 3.22
91.07 4.02
10
50%
81.39 1.98
N2.31 +: 2.82
79.96 d: 3.15
82.16 de 4.64
90.22 +: 3.39
50.80 3.52
90.76
+ 2.58
79.64
2.87
90.12 +
5%
80.33 -: 2.34
81.40 + 3.44
79.37 :: 3.55
81. 3.66
90:03 -: 2.91
89.45 3.16
Table F1: Comparison of different discrepancy feature designs used the Discrepancy Estimator under full fine-tuning on the different datasets with 100% labeled data as Table 2 Discrepancy features are defined as follows: (a) Sequence-level MSE scalar - average MSE over all time steps and channels; (b) Channel-wise MSE vector 16-dimensional vector of channel-specifie MSEs;
(c) Point-wise error sequence - 256-dimensional vector of per-timestep reconstruction errors; (d)
Cluster-wise distance latent-space distance to nearest cluster center from external normal data.
Discrepancy Feature
AD AUROC TDBrain AUROC
PTB AUROC MIMIC AUROC
Sequence-level MSE scalar (ours)
98.03
97.63
95.56
91.56
(b) Channel wise MSE vector
97.36
97.58
95.44
90.18
(c) Point-wise wise error sequence
96.71
96.86
95.37
90.22
Cluster-wise distunce
96.28
96.44
94.95
89.14
These results indicate that our training pipeline is efficient, and the final model is lightweight enough for practical deployment.
G.2 Model Architecture Details
To improve reproducibility, we provide detailed descriptions of the model architecture used in our experiments. The encoder is a dilated convolutional network optionally equipped with multi head attention modules. The projection head is used for final classification. Table Gl summarizes the full configuration.
29

---

Table G1: Architecture details of DAAC used in our experiments.
Component
Setting
Encoder Type
DaulMutiHHeadT'Ennoee
Input Dimensions
Tpek-specific (e.g., 1 or 2 for AD)
Output Dimensions
320
Hidden Dimensions
64
Encoder Depth
10
Number of Heads
2
Head Dimension
160
Channel Dimension
320
Activation Function
ReLU (implicit via submodules)
Parameter Count (AD)
946,368
Table HI: Full fine-tuning results of MIMIC datasets. We use 100% and 10% of labeled data for fine-tuning. Model names are consistent with Table I underline denotes the second best.
Bold indicates the best performance, and
Datasets
Fraetion
Models
Accuracy
Precision
Recall
Fl score
AUROC
AUPRC
TS2vee
78.25
1.66
79.82
72
13.06 +
81.30
1.59
83.17
TF-C
79.03 + 2.43
78.90
3.04
74.08 + 4.04
75.77 3.50
80.22 + 5.22
81.15 + 4.00
Mixing-up
+
81.36
1.67
77.20=2000
86.12
81.83
TS-TCC
75.01 3.55
77.28
5.08
60.46 5.85
71.08
6.85
82.20
80.14 + 3.00
SimCLR
76.85 1.32
78.40
1.80
70.89
2:95
73.22
2.88
1.89 81.55
68.94 à: 2.36
CLOCS
78.00 2.76
80.56
2.81
75.05
4.79
77.71
4.77
87.01
2.07
85.12 + 3.36
COMET
1.98
81.67
1.72
78.14
1: 3.68
3.22 50.45
1.56 89.05
86.40 2.82
COMET=DE
80.65
2.06
82.01
2.37
79.49
1: 4.59
81.67
4.07
89.88
1.94
87.30 1: 3.20
COMET+ACL
81.30
2.03
82.89
2,83
80.10
4.09
62.73
3.82
90.68
2.27
88.02 3.05
MIMIC
DAAC
2.35 82,50
83,8
1.86
81.39
3.84
84.64
3.27
91.56
1.57
88.98 1: 3.49
TSIve
76,32 1.71
75.39
5.01
71. - 2.99
12.35
28 1.04
85.03
= 1.03
83.12 = U.9T
TF-C
77.02 1:23
76.80
2.35
72.84
1.11
74.09
1.14
79.14 1: 6.00
80.07 5.01
Mixing-up
77.90 1.41
3.21 78.56
71.61 + 2.68
76.22
1.99
85,28
1.43
83.22 = 2.76
TS-TCC
74.21 1.53
76.11
24
70.27
2.64
81.06
1.76
79.34
SimCLR
7.10
76.19 = 1.31
68
1.63
T1.40
1.77 77.34
80.19
1.31
66.85 = 3.81
78.33
79.64
75.30 +
4.03
86.91
2.40
84.76
COMET
79.12 3:25
80.18 = 2.60
76.85 + 6.00
T8.91
5.61
88.83 1.08
86.12 2.22
COMET-DE
30.00
2.47
79.46
89.30 2.62
26.26
COMET+ACI.
80.10 -:000---200 2.90
81.21 2.88
78.95 + 2.89
$1.35 3.81
89.90
88.82 + 3.71
DAAC
2.77
82.66
80.23
2.65
3.56
90.30 3.22
91.07
H
Complementary Experiments
To further validate the robustness and gencralizability of our approach, we conduct additional experiments on the MIMIC-IV-ECO dataset for alcoholic liver disease prediction, following the preprocessing protocol from
Alcaraz al. 2025) As shown in Table HI DAAC consistently achieves the best performance
all metries under both 100% and 10% labeled settings. In low-resource regime (10%), DAAC surpasses second best method (COMET+ACL) by +1 +1.2% AUROC and +2.2% AUPRC, demonstrating superior label efficiency. These results confirm that DAAC generalizes well to real-world ECG signals and remains effective even under limited supervision.
30


Reviews:
[{"review_id": "FHiNTpOoFv", "reviewer": "Reviewer_yeTd", "summary": "This 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.", "questions": "1. 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?", "limitations": "The 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.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\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.", "was_revised": true, "final_justification": "My primary concern regarding the method's limited applicability remains.", "rebuttal": "Dear Reviewer,\n\nThank you for your thoughtful and detailed comments. We have carefully addressed each of your concerns below and revised the manuscript accordingly.\n\n**Q1: Concern regarding whether more established datasets, such as MIMIC, were considered for evaluation.**\n\n**A1:** Thank you for this question. While MIMIC is a valuable dataset for EHR studies [1], our model, DAAC, is specifically designed for multi-level electrophysiological time-series (e.g., EEG, ECG) with inherent hierarchical structures (subject, trial, epoch, temporal). The selected AD, PTB, and TDBrain datasets exhibit these structures, providing a suitable testbed for DAAC. Their complexity and use in established benchmarks like COMET [2] ensure a proper and sufficient evaluation of our method's effectiveness (Appendix A.1).\n\n**Q2: Regarding the sensitivity of the contrastive loss weights.**\n\n**A2:** We appreciate this constructive comment and have added a detailed sensitivity analysis in Appendix G.2. To clarify, the 1:1:1:1 weights for the four hierarchical loss components follow prior work [2], which established this as a stable default. Our analysis in added Appendix D2 further supports this configuration's robustness. For the view-level loss $L_V$, we conducted additional experiments by varying its weight. Limited by the size request, we can only put part of the result herein, more results will be shown in added Table D1,The key findings are:\n- **Effectiveness:** Including $L_V$ significantly improves performance, confirming its necessity.\n- **Saturation:** Performance gains diminish as the weight increases, plateauing around a weight of 2.\n- **Robustness:** A weight of 2 provides consistently strong results across all datasets.\n\nIn summary, our proposed loss design, including the weight for $L_V$, is both effective and robust. We have noted that a joint optimization of all weights is a promising direction for future work, and has been mentioned in revision. \n\n| Dataset | Setting | Weights      | Accuracy      | Precision     | Recall        |\n|---------|---------|--------------|--------------|--------------|---------------|\n| **AD**  | **100** | 1,1,1,1,0    | 84.50±4.46   | 88.31±2.42   | 82.95±5.39    |\n|         |         | 1,1,1,1,1    | 88.50±4.20   | 90.20±3.60   | 87.30±4.90    |\n|         |         | 1,1,1,1,2    | 91.16±4.14   | 92.10±3.66   | 90.50±4.51    |\n|         |         | 1,1,1,1,3    | 91.02±3.15   | 92.06±3.54   | 90.58±5.11    |\n|         | **10**  | 1,1,1,1,0    | 91.43±3.12   | 92.52±2.36   | 90.71±3.56    |\n|         |         | 1,1,1,1,1    | 92.30±2.20   | 92.80±2.10   | 92.10±2.50    |\n|         |         | 1,1,1,1,2    | 92.77±2.35   | 92.92±2.43   | 92.55±2.39    |\n|         |         | 1,1,1,1,3    | 92.86±2.44   | 92.33±3.61   | 92.63±2.01    |\n\n\n**Q3: Details of training process.**\n\n**A3:** Additional training details are now in Appendix G.1. All experiments were run on an NVIDIA RTX 4090 GPU. Our training pipeline is efficient, and the final model is lightweight for deployment. We will release the full code upon acceptance.\n| Dataset   | Stage 1 Time | Stage 2 Time | Stage 3 Time | Deployment VRAM |\n|-----------|--------------|--------------|--------------|------------------|\n| AD        | 1.2h         | 3.2h         | 0.2h         | 4.0 GB           |\n| PTB       | 2.0h         | 6.5h         | 1.8h         | 6.2 GB           |\n| TDBrain   | 1.8h         | 5.5h         | 0.5h         | 5.3 GB           |\n\n**Q4: Using a single sequence-level reconstruction error as the discrepancy feature might be too coarse.**\n\n**A4:** We intentionally use a sequence-level error to ensure robustness during the initial training phase, as this coarse-grained signal prevents overfitting to noise or center-specific patterns from external data. While finer-grained measures like point-wise errors can be sensitive to physiological fluctuations and sensor artifacts [3], a sequence-level discrepancy captures global deviations more reliably. Our experiments (Appendix F) confirm that alternatives like channel-wise or point-wise errors yield inferior performance due to their sensitivity to local noise. This coarse feature acts as an auxiliary signal, and we designed the downstream Adaptive Contrastive Learner to subsequently extract fine-grained representations across multiple levels, ensuring detailed information is fully utilized.\n\n| Discrepancy Feature             | AD AUROC | TDBrain AUROC | PTB AUROC |\n|---------------------------------|----------|---------------|-----------|\n| Sequence-level MSE scalar (ours)| 98.03    | 97.63         | 95.56     |\n| Channel-wise MSE vector         | 97.36    | 97.58         | 95.44     |\n| Point-wise error sequence       | 96.71    | 96.86         | 95.37     |\n| Cluster-wise distance           | 96.28    | 96.44         | 94.95     |\n\n**Q5: Model names in the results tables are unclear.**\n\n**A5:** Thank you for the suggestion. We have revised the model names for consistency and provided clear definitions in the captions of Table 1 and Table 2.\n\n**Q6: The Discrepancy Estimator relies on large external normal datasets, which may be infeasible for rare diseases.**\n\n**A6:** This is an important point. (1) For the common diseases in our study (Alzheimer’s, Parkinson’s, etc.), collecting normal data is generally feasible and low-cost. (2) For rare diseases, we agree acquiring large-scale data is difficult. To assess this, we conducted an ablation study and found that even with only 5% of the healthy data, our model still achieves performance gains. This suggests the Discrepancy Estimator remains beneficial even in resource-constrained scenarios.\n\n**Q7: Could you discuss the potential risk of negative transfer from domain shift?**\n\n**A7:** We agree this is a key consideration. Our external and target datasets do exhibit domain shifts (e.g., average subject age of 63.6 vs. 72.5 years in the AD task [4, 5]). We observed signs of this negative transfer when applying a baseline model, where the AUROC dropped from 94.44 to 93.97 (Table 2). Despite this, DAAC still outperformed all baselines in the same setting. This demonstrates that our Adaptive Contrastive Learner (ACL) effectively mitigates the negative impact from domain-shifted data, showcasing the robustness of DAAC's multi-stage design. We have added this discussion to Appendix C3.\n\n**Q8: The novelty seems limited as the work builds heavily upon COMET.**\n\n**A8:** We acknowledge that we build upon COMET's framework. However, the core innovation of DAAC is the synergistic integration of the Discrepancy Estimator (DE) and the Adaptive Contrastive Learner (ACL) into a three-phase, curriculum-inspired framework. This design follows a coarse-to-fine progression:\n- **Phase 1:** DE learns a coarse, abnormality-aware signal from external data.\n- **Phase 2:** ACL uses this signal to dynamically discover and contrast fine-grained views.\n- **Phase 3:** The encoder is fine-tuned for the downstream task.\nThe synergy of these components is vital. Our ablation studies (Table 2) show that simply adding DE or ACL to COMET yields accuracies of 84.82% and 91.16% respectively, while the integrated DAAC framework achieves 93.23%. This demonstrates that the framework itself is a meaningful advancement.\n\nThank you again for your patience and invaluable feedback.\n\nBest regards,\n\nThe Authors\n\n**References**\n\n[1] Johnson, A. E. W., et al. (2021). MIMIC-IV, a freely accessible electronic health record dataset. *Scientific Data*.\n\n[2] Wang, Y., et al. (2024). Contrast everything: A hierarchical contrastive framework for medical time-series. *NeurIPS*.\n\n[3] Liu, Z., et al. (2023). Self-supervised contrastive learning for medical time series: A systematic review. *Sensors*.\n\n[4] Miltiadous, A., et al. (2023). A dataset of EEG recordings from: Alzheimer’s disease, Frontotemporal dementia and Healthy subjects. *OpenNeuro*.\n\n[5] Escudero, J., et al. (2006). Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy. *Physiological Measurement*."}, {"review_id": "wpbBP6gUo5", "reviewer": "Reviewer_w2Dz", "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.", "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. \nWhich part do you think contribute the most to the final improvement?\nCan you define kind and source of data, including normal data/external data/target data/original data", "limitations": "As above", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength\nThe paper is focusing on a very important domain\nThe experiments demonstrating fine-tuning for downstream classification tasks are comprehensively presented in the paper.\nThe proposed method demonstrates strong performance in downstream classification tasks, even with limited labeled data.\n\nWeakness\nThere 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. \nThe experimental setup is not clear, somehow confusing.\nThere are many abbreviations  has no full name, especially for the ablations, which makes the effectiveness of each part very hard to interpret.", "was_revised": true, "final_justification": "I recommend 4. The reviewer have solved my concerns.", "rebuttal": "Dear Reviewer,\n\nWe sincerely appreciate your thoughtful review and helpful suggestions. We have addressed all comments and provided detailed responses below.\n\n**Q1: This paper use many terms refers data types (e.g., normal/external/target/original data) and includes unexplained abbreviations (especially in ablation studies), leading to confusion about the Discrepancy Estimator’s functionality and experimental setup.**\n\n**A1:** Thank you for your suggestion. We have revised the model names for consistency and clarity, and provided clear definitions for each model in the captions of Table 1 and Table 2. Also, we additionally provide explanations for all key terms about data types in Appendix C.\nIn the medical field, datasets from different hospitals often exhibit a certain degree of distributional shift. Models trained on data from one hospital may demonstrate unstable performance when applied to others. To enhance model generalization, we propose leveraging DE (Discrepancy Estimator) to incorporate data from other hospitals. Although distributional differences exist, we argue that the sequential patterns of normal data (i.e., data from healthy patients) can still provide valuable guidance for diagnosis in the target hospital.\nIn this study:\n- The dataset from the current hospital is referred to as the internal dataset (or target dataset), which participates in the second and third stages of model training.\n- Datasets from other hospitals, used for guidance, are termed external datasets which are from healthy patients and contribute to the first stage of training.\n\n**Q2: 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?**\n\n**A2:** Thank you for raising this thoughtful question. We agree that the discrepancy feature serves as an auxiliary signal to enrich the original data representation. Our choice to use the sequence-level reconstruction error stems from the need for a robust signal that generalizes well under the potential distribution shift from the external dataset. Specifically, this design:\n- Avoids overfitting to local noise or domain-specific patterns, which can occur when using fine-grained or structure-sensitive alternatives (e.g., point-wise or cluster-based distances).\n- Aligns with our coarse-to-fine training strategy, where this global signal (i.e., discrepancy) guides the early stage and is progressively refined by the Adaptive Contrastive Learner (ACL) in later stages.\nTo validate this choice, we further conducted ablation experiments (see Appendix F) comparing our method with alternative discrepancy features, including:\n- Channel-wise MSE vector (16-dim),\n- Point-wise reconstruction error sequence (256-dim),\n- Cluster-wise latent distance to normal data.\nAs shown in Table F1, while these alternatives incorporate more structural or local cues, they consistently underperform compared to our scalar discrepancy. We attribute this to their higher sensitivity to noise and domain shift, which can degrade early-stage learning.\nIn summary, our discrepancy design is not arbitrary, which reflects a careful tradeoff between stability, informativeness, and generalizability, and is empirically validated across diverse datasets and settings.\n\n| Discrepancy Feature                | AD AUROC | TDBrain AUROC | PTB AUROC |\n|:----------------------------------:|:--------:|:-------------:|:---------:|\n| Sequence-level MSE scalar (ours)   | 98.03    | 97.63         | 95.56     |\n| Channel-wise MSE vector            | 97.36    | 97.58         | 95.44     |\n| Point-wise error sequence          | 96.71    | 96.86         | 95.37     |\n| Cluster-wise distance              | 96.28    | 96.44         | 94.95     |\n\n\n**Q3: Which part do you think contribute the most to the final improvement?**\n\n**A3:** Thank you for the valuable question. As shown in Table 2, the Adaptive Contrastive Learner (ACL) provides the most consistent and significant improvements across all datasets under the full supervision setting (100% labeled data). However, under limited-label conditions (e.g., 10% labeled data), the Discrepancy Estimator (DE) becomes increasingly important.\nTherefore, the performance gain of DAAC comes from the synergistic integration of both components, rather than relying on a single dominant module. Specifically, DAAC’s effectiveness is amplified through:\n- Coarse anomaly guidance from DE, which helps ACL focus on more informative signals.\n- Multi-head adaptive view construction in ACL, enabling more diverse and robust contrastive pairs for better representation learning.\nWe emphasize that the largest performance gains emerge when DE and ACL are progressively combined in a coarse-to-fine manner, as demonstrated by the full DAAC outperforming partial variants such as COMET+DE and COMET+ACL.\n\nWe are deeply grateful for the your constructive feedback. We hope that our detailed response provided above can address your questions and suggestions.\n\nBest regards,\n\nThe Authors"}, {"review_id": "6bo9EwUub3", "reviewer": "Reviewer_LgXM", "summary": "The 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.", "questions": "### 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.", "limitations": "No. 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.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "### 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.", "was_revised": true, "final_justification": "The authors are to be commended for performing additional experiments, such as ablation studies, which contribute positively to the empirical assessment.\nThat said, the explanation of the criteria underlying model selection is incomplete.\nWhile part of the concern has been addressed, prompting me to raise my overall rating, I still judge the work to fall short of the acceptance threshold.", "rebuttal": "Dear Reviewer,\n\nWe extend our sincere appreciation for your time and expertise in reviewing our work. Our detailed responses to the reviewers' comments are presented below.\n\n**Q1: Minor issues – typo in ‘surpervised’; unclear bold formatting in Table 2.**\n\n**A1:** Thank you for pointing these out. We have corrected the typo \"surpervised\" to \"supervised\" in the revised figure. Also, We have clarified the table formatting: \"boldface now consistently indicates the best performance per task. underline denotes the second best\". Meanwhile, we have updated the captions of Table 1 and Table 2 accordingly to avoid term confusion, which will largerly improve our clarity. We appreciate your careful reading and helpful suggestions.\n\n**Q2: 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**A2:** We appreciate the reviewer’s concern regarding the feasibility and value of using a large-scale healthy population pool. To address this, we conducted ablation experiments on all three datasets (AD, PTB, TDBrain), varying the proportion of healthy individuals used for pretraining (e.g., 5%, 25%, 50%, and 100%), as shown in Table E3. For each proportion, we further evaluated performance under different amounts of labeled training data (5%, 25%, 50%, 100%). Due to space constraints, we only present the results for the AD, PTB dataset in the main text, but consistent trends were observed across all datasets. We have updated Appendix E3 to clarify this point and provide additional results.\nKey findings:\n- Across all datasets and settings, increasing the size of the healthy population consistently leads to better model performance.\n- The improvement is existed even in low-resource settings, where the contrastive pretraining helps compensate for limited supervision.\n- The performance trend is stable and monotonic, suggesting that more external healthy data continues to benefit the model without introducing instability.\n- These results confirm that the proposed approach can adapt to different scales of external data and highlight the practical value of incorporating healthy population information, even when collected in limited amounts.\nThis validates the scalability and deployment feasibility of our method, and demonstrates that the use of external healthy individuals is both effective and justifiable.\n\n| Dataset | Label Fraction (%) | External Ratio |    Accuracy     |    Precision    |     Recall      |    F1 score     |     AUROC       |    AUPRC        |\n|---------|-------------------|---------------|-----------------|-----------------|-----------------|-----------------|-----------------|-----------------|\n|   AD    |        100        |    100%       | **93.23 ± 5.25**| **94.01 ± 3.98**| **92.71 ± 5.86**| **92.97 ± 5.65**| **98.03 ± 1.71**| **98.03 ± 1.75**|\n|         |                   |    50%        | 92.21 ± 3.89    | 93.48 ± 3.94    | 92.22 ± 4.61    | 92.70 ± 4.13    | 97.10 ± 3.21    | 97.96 ± 3.28    |\n|         |                   |    25%        | 91.45 ± 4.21    | 93.10 ± 4.55    | 91.73 ± 4.33    | 92.04 ± 4.14    | 96.84 ± 3.95    | 96.67 ± 4.22    |\n|         |                   |    5%         | 91.23 ± 4.01    | 92.15 ± 3.06    | 90.70 ± 4.01    | 91.88 ± 4.33    | 96.34 ± 2.22    | 96.13 ± 2.23    |\n|         |        10         |    100%       | **94.67 ± 1.84**| **94.93 ± 1.76**| **94.41 ± 1.99**| **94.58 ± 1.89**| **98.10 ± 1.20**| **98.19 ± 1.15**|\n|         |                   |    50%        | 93.24 ± 2.86    | 94.01 ± 2.91    | 93.76 ± 2.63    | 93.22 ± 2.67    | 98.01 ± 1.72    | 97.94 ± 1.88    |\n|         |                   |    25%        | 92.93 ± 3.19    | 93.56 ± 3.25    | 93.24 ± 3.60    | 93.17 ± 3.14    | 97.87 ± 2.10    | 97.66 ± 2.41    |\n|         |                   |    5%         | 92.87 ± 2.35    | 93.02 ± 2.43    | 92.89 ± 2.37    | 93.42 ± 2.87    | 97.50 ± 1.45    | 97.49 ± 1.57    |\n|                               |                |                     |             |             |           |                |\n| PTB   | 100   | 100%           | **93.65 ± 2.35**| **93.28 ± 1.86**| **85.39 ± 3.84**| 87.64 ± 3.27    | **95.56 ± 1.57**| **90.28 ± 3.49**|\n|       |       | 50%            | 92.60 ± 2.12    | 92.89 ± 2.63    | 85.30 ± 4.36    | 85.71 ± 3.44    | 95.30 ± 2.34   | 90.20 ± 3.47   |\n|       |       | 25%            | 92.12 ± 3.15    | 92.55 ± 2.90    | 84.54 ± 5.19    | 85.69 ± 4.31    | 94.27 ± 2.83   | 90.16 ± 3.98   |\n|       |       | 5%             | 91.50 ± 2.03    | 91.89 ± 2.83    | 84.40 ± 4.09    | 85.67 ± 3.71    | 94.23 ± 1.94   | 90.05 ± 3.20   |\n|       | 10    | 100%           | **91.35 ± 2.77**| **91.56 ± 3.12**| **85.23 ± 2.65**| **85.61 ± 3.56**| **96.30 ± 3.22**| **97.87 ± 4.02**|\n|       |       | 50%            | 91.05 ± 3.10    | 91.19 ± 2.96    | 84.91 ± 3.92    | 85.55 ± 3.62    | 96.26 ± 3.42   | 96.88 ± 3.55   |\n|       |       | 25%            | 90.93 ± 3.48    | 90.61 ± 3.27    | 84.52 ± 4.34    | 85.48 ± 3.89    | 96.07 ± 3.88   | 96.03 ± 4.12   |\n|       |       | 5%             | 90.80 ± 2.93    | 90.06 ± 2.47    | 83.80 ± 4.86    | 85.43 ± 3.27    | 95.91 ± 3.51   | 95.80 ± 3.39   |\n\n\n**Q3: 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**A3:** We appreciate the reviewer’s suggestion and have conducted additional analysis to empirically validate the assumption. Due to the submission policy disallowing figures in the rebuttal, we provide a textual description of the key results and refer the reviewer to Figure F1 (has been added to the revised manuscript) in Appendix F.2 for visual confirmation.\n\nWe performed Kernel Density Estimation (KDE) on the reconstruction errors of both normal and abnormal samples, and showed in Figure F1. The normal samples exhibit a sharp peak centered around a low reconstruction error (\\~0.2), with very narrow spread, indicating consistent low-error reconstructions. In contrast, the abnormal samples follow a positively skewed distribution, with reconstruction errors more broadly distributed and a long tail extending toward higher values (\\~1.0). This empirical result confirms that the model consistently assigns higher reconstruction errors to abnormal samples.\n\nFurthermore, using the reconstruction error as the anomaly score yields an AUROC of 0.974 on the AD dataset, quantitatively supporting the model’s discriminative capability between normal and abnormal samples.\n\n\n**Q4：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**A4:** We appreciate the reviewer’s feedback regarding the architectural transparency of our model. To address this, we have included detailed architectural specifications in Table G1 now clearly outlines the encoder type, dimensional configurations (input/output/hidden/channel/head), activation function, and parameter count.\nThese additions ensure that all design choices—layer counts, attention structure, and parameter sizes—are explicitly stated.\n| Component                    | Setting                                   |\n|:----------------------------:|:-----------------------------------------:|\n| Encoder Type                 | DaulMultiHeadTSEncoder                    |\n| Input Dimensions             | Task-specific (e.g., 1 or 2 for AD)       |\n| Output Dimensions            | 320                                       |\n| Hidden Dimensions            | 64                                        |\n| Encoder Depth                | 10                                        |\n| Number of Heads              | 2                                         |\n| Head Dimension               | 160                                       |\n| Channel Dimension            | 320                                       |\n| Activation Function          | ReLU (implicit via submodules)            |\n| Parameter Count (AD)         | 946,368                                   |\n\n**Q5: The privacy considerations involved even when using “healthy” clinical data.**\n\n**A5:** We agree that even healthy clinical data may raise privacy concerns. As noted in our experimental section, all datasets used in this study are publicly available and had received institutional review board (IRB) approval prior to release, which ensures appropriate de-identification and regulatory compliance (e.g., HIPAA, GDPR). \n\n**Reply to the concerns on significance and clarity**\n\nWe really appreciate for your valuable insights. These comments allow us to further improve our work quality via revision. We hope the responses have fully addressed your questions and provide strong support for the significance of our method. With all these designs, our approach demonstrates significant performance improvements across multiple datasets (AD, PTB, TDBrain) even in a low-resource setting. For example, on the AD dataset, even with only 5% of external healthy data, the model could significantly outperform the baseline without DE pretraining. This validates the effectiveness and practical adaptability of our framework.\nIn addition, we really care about your feedback regarding clarity. Accordingly, we have revised our paper seriously, which includes clarifying the method definitions (see Table 1), architectural details (see Table G1), and experimental setup (see Appendices E and F), etc. In addition, a native speaker is asked to further improve the narratives in the manuscript. We suppose these endeavors could help to alleviate your clarity concern. Once more, thank you very much for your time and suggestions for reviewing our paper. \n\nBest regards,\n\nThe Authors"}, {"review_id": "GQw7aTaR9r", "reviewer": "Reviewer_RMcN", "summary": "The 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.", "questions": "Q1) 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?", "limitations": "The authors state that “We explicitly discuss limitations in Section 6”, but the main text ends with “5 Conclusion”. Where is the section 6?", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \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.", "was_revised": true, "final_justification": "After thoughtful rebuttal discussion, I keep my rating.", "rebuttal": "Dear Reviewer,\n\nThank you very much for your thoughtful review and constructive feedback on our submission. We truly value the time and effort you have dedicated to evaluating our work. Please find our point-by-point responses to your comments below.\n\n**Q1: Discrepancy signal granularity**\n\n**A1:** Thank you for raising this insightful question.\nWe intentionally design the discrepancy feature as a sequence-level reconstruction error to achieve greater robustness and generalization, especially in the first phase of our three-stage training pipeline. This design aligns with our coarse-to-fine learning strategy, where early stages focus on global guidance and later stages handle finer representations.\nWhile finer-grained signals (e.g., point-wise or channel-wise errors) may capture local anomalies, the presence of distribution shifts between external and target datasets can make these fine-grained discrepancies prone to overfitting. This may introduce harmful biases into the downstream representation learning of the ACL module and compromise its generalization ability.\nTo validate our design choice, we implemented and compared the following discrepancy feature variants. Results are included in Appendix E Discrepancy Estimator Study:\nAs shown in the table above, while alternatives Channel-wise MSE vector and Point-wise error sequence capture more local detail, they yield inferior performance compared to our scalar sequence-level discrepancy. We attribute this to their sensitivity to non-generalizable local noise patterns from external data, which can introduce instability in early-stage training and hinder the learning of transferable features.\nMoreover, we emphasize that this discrepancy signal is only used in the first stage as a weak, auxiliary cue. It is later refined and complemented by the multi-level Adaptive Contrastive Learner (ACL), which captures detailed temporal and spatial structures.\nTherefore, our choice reflects a principled balance between stability, generality, and progressive refinement, and we find it to be both effective and practical across diverse tasks.\n\n| Discrepancy Feature               | AD AUROC | TDBrain AUROC | PTB AUROC |\n|-----------------------------------|----------|---------------|-----------|\n| Sequence-level MSE scalar (ours)  | **98.03** | **97.63**     | **95.56** |\n| Channel-wise MSE vector           | 97.36    | 97.58         | 95.44     |\n| Point-wise error sequence         | 96.71    | 96.86         | 95.37     |\n| Cluster-wise distance             | 96.28    | 96.44         | 94.95     |\n\n\n**Q2: Loss complexity and its hyperparameter stability**\n\n**A2:**  We appreciate this constructive comment and have added a detailed sensitivity analysis in Appendix G.2. To clarify, the 1:1:1:1 weights for the four hierarchical loss components follow prior work [2], which established this as a stable default. Our analysis in added Appendix D2 further supports this configuration's robustness. For the view-level loss $L_V$, we conducted additional experiments by varying its weight. Limited by the size request, we can only put part of the result herein, more results will be shown in added Table D1,The key findings are:\n- **Effectiveness:** Including $L_V$ significantly improves performance, confirming its necessity.\n- **Saturation:** Performance gains diminish as the weight increases, plateauing around a weight of 2.\n- **Robustness:** A weight of 2 provides consistently strong results across all datasets.\n\nIn summary, our proposed loss design, including the weight for $L_V$, is both effective and robust. We have noted that a joint optimization of all weights is a promising direction for future work.\n\n| Dataset | Setting | Weights      | Accuracy      | Precision     | Recall        |\n|---------|---------|--------------|--------------|--------------|---------------|\n| **AD**  | **100** | 1,1,1,1,0    | 84.50±4.46   | 88.31±2.42   | 82.95±5.39    |\n|         |         | 1,1,1,1,1    | 88.50±4.20   | 90.20±3.60   | 87.30±4.90    |\n|         |         | 1,1,1,1,2    | 91.16±4.14   | 92.10±3.66   | 90.50±4.51    |\n|         |         | 1,1,1,1,3    | 91.02±3.15   | 92.06±3.54   | 90.58±5.11    |\n|         |         |              |              |              |               |\n|         | **10**  | 1,1,1,1,0    | 91.43±3.12   | 92.52±2.36   | 90.71±3.56    |\n|         |         | 1,1,1,1,1    | 92.30±2.20   | 92.80±2.10   | 92.10±2.50    |\n|         |         | 1,1,1,1,2    | 92.77±2.35   | 92.92±2.43   | 92.55±2.39    |\n|         |         | 1,1,1,1,3    | 92.86±2.44   | 92.33±3.61   | 92.63±2.01    |\n\n\n**Q3: About the risk of distribution mismatch between external data and target data**\n\n**A3:** Thank you for this thoughtful and important question.\nWe agree that relying on external normal data assumes a certain level of domain alignment. In practice, however, our external and target datasets exhibit notable differences, including variations in population demographics and acquisition protocols.\nFor example, in the AD task, the external dataset ([1]) has an average subject age of 63.6 years, while the target dataset ([2]) has an average age of 72.5 years. When training the Discrepancy Estimator (DE) using this external dataset, we indeed observe signs of negative transfer—specifically, in Table 2, the AUROC of COMET+DE drops from 94.44 to 93.97.\nDespite this, DAAC still outperforms both COMET+ACL and COMET+DE in the same setting, as shown in Table 2. This suggests that the Adaptive Contrastive Learner (ACL) effectively mitigates the potential negative impact introduced by domain-shifted external data, and in some cases, even corrects for suboptimal guidance from the DE. This robustness highlights the strength of DAAC’s multi-stage design in handling real-world domain variability. We have added the related discussion in Appendix C3.\n\n**Q4: The proposed method was tested on only 2 medical modalities, EEG and ECG. What are the next target modalities in the medical domain? How about outside of the medical domain?**\n\n**A4:** Thank you for this constructive question. Although our current experiments are limited to EEG and ECG modalities, DAAC is designed as a general and extensible framework for learning representations from hierarchical electrophysiological signals, and is not restricted to specific data types. In future work, we plan to extend DAAC to additional modalities that share similar structural properties, including but not limited to: EMG, ECoG, MEG, fNIRS, PPG, respiratory signals, and GSR.\nThese modalities are widely used in clinical diagnostics, cognitive neuroscience, and human-computer interaction, and typically exhibit clear subject， trial, epoch, and temporal organization, which aligns well with DAAC's multi-level contrastive learning strategy. We believe that applying DAAC to these broader signal types will not only validate the method’s generalizability, but also greatly enhance its practical utility across diverse real-world applications.\nWhile our current work focuses on medical time-series, we believe DAAC can also be applied to domains such as fault diagnosis, predictive maintenance, and battery life prediction. These tasks often involve hierarchical time-series and have abundant healthy data but limited labeled faults—conditions that align well with DAAC’s design, particularly the use of external normal data for discrepancy estimation. We see this as a promising future direction.\n\n**Q5: The authors state that “We explicitly discuss limitations in Section 6”, but the main text ends with “5 Conclusion”. Where is the section 6?**\n\n**A5:** Thank you for pointing this out. This was a formatting oversight in our submission. The limitations are indeed discussed in the final part of Section 5 (Conclusion). We have corrected the section reference and will ensure that this is clearly marked in the revised version.\n\nYour insights have been instrumental in helping us improve the clarity and rigor of our paper. We have carefully addressed each concern and believe that the revised version better reflects the contributions and robustness of our approach.\n\nSincerely,\n\nThe Authors\n\n**References** \n[1] Miltiadous, Andreas, Katerina D. Tzimourta, Theodora Afrantou, Panagiotis Ioannidis, Nikolaos Grigoriadis, Dimitrios G. Tsalikakis, Pantelis Angelidis et al. \"A dataset of EEG recordings from: Alzheimer’s disease, Frontotemporal dementia and Healthy subjects.\" OpenNeuro 1 (2023): 88.\n[2] Escudero, J., Abásolo, D., Hornero, R., Espino, P. and López, M., 2006. Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy. Physiological measurement, 27(11), p.1091."}]

Author rebuttal:

Meta-review: This paper proposes a hierarchical contrastive learning framework for specific kinds of medical time series. The experimental results and the proposed approach's ability to handle limited labels are key strengths. I've taken a look at the reviews and the author/reviewer discussion comments for this paper. The final ratings and justifications are a little bit scattered (in terms of ratings: 1 accept, 1 borderline accept, 2 borderline reject). From what I can tell, the bulk of reviewer concerns were addressed by the authors. Reviewer yeTd's major lingering concern seems to be about limited applicability (if I'm not mistaken, the issue seems to be that the proposed approach is only for time series with specific structure such as EEG and ECG): I'm not as bothered by this especially as there are many researchers focused on EEG and ECG time series (I understand that there are a bunch of other time series that are very different in structure, such as what comes from many wearable sensors or, separately, EHR data like MIMIC), so that this work could be beneficial to the community of ML (and ML-adjacent) researchers focused on EEG or ECG. Regarding reviewer LgXM's main lingering concern that the authors did not explain the specific choice of transformer architecture, I find this to be a valid albeit relatively minor concern, and I am okay with the authors' response that they'll discuss other things they tried for model selection in the camera ready. For these reasons, I am recommending acceptance for this paper.
Decision: Accept (poster)