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VUNO Inc.

Industry researchasia · kr
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Research library9linked papers
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Selected work

Representative Papers

Label-Efficient Deep Learning for ECG Delineation: A Multi-Dataset Benchmark against Widely Used Delineation Tools

Oct 06, 2026

This study addresses the reliance of ECG segmentation on costly expert annotations and the uncertain performance of existing tools by proposing a label-efficient deep learning framework. Methodologically, it systematically evaluates combination strategies of self-supervised pre-training and semi-supervised fine-tuning, benchmarking them against established tools such as NeuroKit2 across multiple datasets. The primary contribution lies in validating the effectiveness of these strategies for identifying ECG waveform boundaries, thereby providing empirical guidance for clinical model selection. Experimental results demonstrate that the proposed model comprehensively outperforms existing baselines across multiple evaluation metrics and datasets, exhibiting particularly significant advantages in scenarios characterized by diverse cardiac rhythms.

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Bidirectional Fusion Guided by Cardiac Patterns for Semi-Supervised ECG Segmentation

May 15, 2026

This work addresses the scarcity of annotated data in electrocardiogram (ECG) segmentation by proposing CardioMix, a framework that introduces a cardiac rhythm–guided bidirectional CutMix strategy to enable physiologically consistent data augmentation between labeled and unlabeled samples. By explicitly modeling the temporal patterns of cardiac electrical activity, CardioMix ensures that augmented samples maintain physiological plausibility. Designed as a plug-and-play module, it seamlessly integrates with prevailing semi-supervised segmentation algorithms. Evaluated on the SemiSegECG multi-dataset benchmark, CardioMix consistently outperforms existing CutMix variants across varying label proportions, achieving robust and state-of-the-art segmentation performance.

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Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation

May 11, 2026

This study addresses the pervasive issue of label noise in medical imaging datasets, primarily caused by inter-observer variability and ambiguous samples. To tackle this challenge, the authors propose the Standardized Loss Aggregation (SLA) framework, which iteratively aggregates standardized fold-level validation losses across repeated cross-validation runs. By jointly characterizing the frequency and magnitude of performance deviations, SLA generates statistically stable and interpretable noise scores without requiring task-specific adaptations. Experimental results on public fundus image datasets demonstrate that SLA consistently outperforms existing baselines across varying noise levels, exhibiting particularly faster convergence and superior performance in low-noise regimes, thereby significantly enhancing data annotation quality.

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A Comparative Study of Machine Learning and Deep Learning for Out-of-Distribution Detection

May 11, 2026

This study addresses the challenge of efficiently and accurately detecting out-of-distribution (OOD) samples in visually constrained medical imaging scenarios to enhance AI system reliability. The authors systematically evaluate the OOD detection performance of both traditional machine learning and deep learning approaches on a dataset comprising over 60,000 multi-resolution fundus and non-fundus images. Experimental results demonstrate that both method families achieve near-perfect AUROC (1.000) and accuracy (0.999–1.000) on in-distribution and out-of-distribution validation sets. Notably, lightweight machine learning models attain comparable detection accuracy while significantly reducing inference latency, highlighting their practical utility in resource-constrained clinical applications.

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Recent publications

Latest Papers

Label-Efficient Deep Learning for ECG Delineation: A Multi-Dataset Benchmark against Widely Used Delineation Tools

Oct 06, 2026

This study addresses the reliance of ECG segmentation on costly expert annotations and the uncertain performance of existing tools by proposing a label-efficient deep learning framework. Methodologically, it systematically evaluates combination strategies of self-supervised pre-training and semi-supervised fine-tuning, benchmarking them against established tools such as NeuroKit2 across multiple datasets. The primary contribution lies in validating the effectiveness of these strategies for identifying ECG waveform boundaries, thereby providing empirical guidance for clinical model selection. Experimental results demonstrate that the proposed model comprehensively outperforms existing baselines across multiple evaluation metrics and datasets, exhibiting particularly significant advantages in scenarios characterized by diverse cardiac rhythms.

0 citationsRead paper

Bidirectional Fusion Guided by Cardiac Patterns for Semi-Supervised ECG Segmentation

May 15, 2026

This work addresses the scarcity of annotated data in electrocardiogram (ECG) segmentation by proposing CardioMix, a framework that introduces a cardiac rhythm–guided bidirectional CutMix strategy to enable physiologically consistent data augmentation between labeled and unlabeled samples. By explicitly modeling the temporal patterns of cardiac electrical activity, CardioMix ensures that augmented samples maintain physiological plausibility. Designed as a plug-and-play module, it seamlessly integrates with prevailing semi-supervised segmentation algorithms. Evaluated on the SemiSegECG multi-dataset benchmark, CardioMix consistently outperforms existing CutMix variants across varying label proportions, achieving robust and state-of-the-art segmentation performance.

0 citationsRead paper

Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation

May 11, 2026

This study addresses the pervasive issue of label noise in medical imaging datasets, primarily caused by inter-observer variability and ambiguous samples. To tackle this challenge, the authors propose the Standardized Loss Aggregation (SLA) framework, which iteratively aggregates standardized fold-level validation losses across repeated cross-validation runs. By jointly characterizing the frequency and magnitude of performance deviations, SLA generates statistically stable and interpretable noise scores without requiring task-specific adaptations. Experimental results on public fundus image datasets demonstrate that SLA consistently outperforms existing baselines across varying noise levels, exhibiting particularly faster convergence and superior performance in low-noise regimes, thereby significantly enhancing data annotation quality.

0 citationsRead paper

A Comparative Study of Machine Learning and Deep Learning for Out-of-Distribution Detection

May 11, 2026

This study addresses the challenge of efficiently and accurately detecting out-of-distribution (OOD) samples in visually constrained medical imaging scenarios to enhance AI system reliability. The authors systematically evaluate the OOD detection performance of both traditional machine learning and deep learning approaches on a dataset comprising over 60,000 multi-resolution fundus and non-fundus images. Experimental results demonstrate that both method families achieve near-perfect AUROC (1.000) and accuracy (0.999–1.000) on in-distribution and out-of-distribution validation sets. Notably, lightweight machine learning models attain comparable detection accuracy while significantly reducing inference latency, highlighting their practical utility in resource-constrained clinical applications.

0 citationsRead paper