SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation | Paper Citation Record

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This paper cites Isp ecg delineation dataset, 2024.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Isp ecg delineation dataset, 2024

Reference 1

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This paper cites Ecgvednet: A variational encoder-decoder network for ecg delineation in morphology variant ecgs.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Ecgvednet: A variational encoder-decoder network for ecg delineation in morphology variant ecgs

Reference 2

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This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

Reference 3

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This paper cites Semi-supervised semantic segmentation with cross pseudo supervision.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Semi-supervised semantic segmentation with cross pseudo supervision

Reference 4

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This paper cites Post-processing refined ecg de- lineation based on 1d-unet.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Post-processing refined ecg de- lineation based on 1d-unet

Reference 5

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This paper cites Randaugment: Practical automated data augmen- tation with a reduced search space.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Randaugment: Practical automated data augmen- tation with a reduced search space

Reference 6

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation An image is worth 16x16 words: Transformers for image recognition at scale

Reference 7

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Observation a759f83e-e220-472c-8ff8-90f6f03cc01e · outbound

This paper cites Revisiting qrs detection methodologies for portable, wearable, battery-operated, and wireless ecg sys- tems.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Revisiting qrs detection methodologies for portable, wearable, battery-operated, and wireless ecg sys- tems

Reference 8

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This paper cites ECG signal processing, classification and interpretation: a comprehensive frame- work of computational intelligence.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation ECG signal processing, classification and interpretation: a comprehensive frame- work of computational intelligence

Reference 9

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This paper cites Physiobank, physiotoolkit, and physionet: compo- nents of a new research resource for complex physiologic signals.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Physiobank, physiotoolkit, and physionet: compo- nents of a new research resource for complex physiologic signals

Reference 10

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Observation d718b51c-49b0-40f2-acd3-59bde7c9b76e · outbound

This paper cites Deep residual learning for image recognition.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Deep residual learning for image recognition

Reference 11

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Observation 1a4705b8-8afd-469d-ae35-3bca8b866142 · outbound

This paper cites U-net architecture for the automatic detection and de- lineation of the electrocardiogram.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation U-net architecture for the automatic detection and de- lineation of the electrocardiogram

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:37:29.247842Z

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This paper cites Delineation of the electrocardiogram with a mixed- quality-annotations dataset using convolutional neural net- works.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Delineation of the electrocardiogram with a mixed- quality-annotations dataset using convolutional neural net- works

Reference 13

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This paper cites Generalising electrocardiogram detec- tion and delineation: training convolutional neural networks with synthetic data augmentation.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Generalising electrocardiogram detec- tion and delineation: training convolutional neural networks with synthetic data augmentation

Reference 14

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This paper cites Deep learning based ecg segmentation for delineation of diverse arrhyth- mias.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Deep learning based ecg segmentation for delineation of diverse arrhyth- mias

Reference 15

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Observation 6dcbbef9-200e-4016-8017-840afee3a4fc · outbound

This paper cites Ludb: a new open-access validation tool for electrocar- diogram delineation algorithms.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Ludb: a new open-access validation tool for electrocar- diogram delineation algorithms

Reference 16

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This paper cites Imagenet classification with deep convolutional neural net- works.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Imagenet classification with deep convolutional neural net- works

Reference 17

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This paper cites A database for evaluation of algorithms for mea- surement of qt and other waveform intervals in the ecg.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation A database for evaluation of algorithms for mea- surement of qt and other waveform intervals in the ecg

Reference 18

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This paper cites Efficient data augmentation policy for electrocardiograms.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Efficient data augmentation policy for electrocardiograms

Reference 19

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Observation d18cb9bf-8481-4f44-95ca-252d56748d2b · outbound

This paper cites Ecg segnet: An ecg delineation model based on the encoder-decoder structure.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Ecg segnet: An ecg delineation model based on the encoder-decoder structure

Reference 20

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Observation b4340325-ceff-417b-ab81-5e4ead9a9e73 · outbound

This paper cites Specialized ecg data augmentation method: leveraging precordial lead positional variability.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Specialized ecg data augmentation method: leveraging precordial lead positional variability

Reference 21

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Observation d9814ac7-9928-4025-a5a1-0a38f8da8f81 · outbound

This paper cites Performance analy- sis of ten common qrs detectors on different ecg application cases.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Performance analy- sis of ten common qrs detectors on different ecg application cases

Reference 22

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Observation 949776be-5e69-4247-85f0-df194309f27d · outbound

This paper cites Bootstrapping semantic segmentation with re- gional contrast.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Bootstrapping semantic segmentation with re- gional contrast

Reference 23

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Observation 2dd8c9ae-9284-4072-a944-5202b0133fdd · outbound

This paper cites Fully convolutional networks for semantic segmentation.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Fully convolutional networks for semantic segmentation

Reference 24

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This paper cites SGDR: Stochastic gradi- ent descent with warm restarts.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation SGDR: Stochastic gradi- ent descent with warm restarts

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:37:28.872457Z

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Observation a982e391-f07c-4333-bfd8-70c41d6fb43c · outbound

This paper cites Decoupled weight de- cay regularization.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Decoupled weight de- cay regularization

Reference 26

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Observation 35343052-eb77-46a5-80c2-889aabcbdd04 · outbound

This paper cites A wavelet-based ecg delineator: evaluation on standard databases.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation A wavelet-based ecg delineator: evaluation on standard databases

Reference 27

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Observation b0b13ff5-179d-4d86-83f6-71c89f497414 · outbound

This paper cites Guiding masked representation learning to capture spatio- temporal relationship of electrocardiogram.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Guiding masked representation learning to capture spatio- temporal relationship of electrocardiogram

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:37:28.758033Z

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Observation e014f2c1-3b9a-4b95-8f30-af7fd0a8ad3f · outbound

This paper cites Data Augmentation for Electrocardiogram Classification with Deep Neural Network.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Data Augmentation for Electrocardiogram Classification with Deep Neural Network

Reference 29

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Observation 9b375efd-8ea1-4838-a3a4-1a127c0954a7 · outbound

This paper cites A Survey on Semi-Supervised Semantic Segmentation.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation A Survey on Semi-Supervised Semantic Segmentation

Reference 30

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Observation 90d34179-dd9b-4f0a-834f-850a7f90754d · outbound

This paper cites A systematic survey of data augmentation of ecg signals for ai applications.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation A systematic survey of data augmentation of ecg signals for ai applications

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:37:28.729520Z

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Observation 35ba9db6-fc2d-458a-abd2-a04927ecb7f5 · outbound

This paper cites Automatic diagnosis of the 12-lead ecg using a deep neural network.Nature commu- nications, 11(1):1760, 2020.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Automatic diagnosis of the 12-lead ecg using a deep neural network.Nature commu- nications, 11(1):1760, 2020

Reference 32

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Observation ef0ca254-0841-4f15-985d-e69c7d3c3a8e · outbound

This paper cites Multiple electrocardiogram generator with single-lead electrocardiogram.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Multiple electrocardiogram generator with single-lead electrocardiogram

Reference 33

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Observation b37e8331-8130-4354-8b78-0112fdc4a6f9 · outbound

This paper cites Fixmatch: Simplifying semi-supervised learning with consistency and confidence.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Fixmatch: Simplifying semi-supervised learning with consistency and confidence

Reference 34

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This paper cites Dropout: a simple way to prevent neural networks from overfitting.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Dropout: a simple way to prevent neural networks from overfitting

Reference 35

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Observation 40928352-5d63-409a-85c8-0ede33e6e53e · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 36

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Observation 192ca11f-1770-4feb-8736-21dc23ef8889 · outbound

This paper cites Training data-efficient image transformers & distillation through at- tention.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Training data-efficient image transformers & distillation through at- tention

Reference 37

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Observation d24cb01c-3618-422f-b6ab-c8c4fec11292 · outbound

This paper cites Normal values of corrected heart- rate variability in 10-second electrocardiograms for all ages.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Normal values of corrected heart- rate variability in 10-second electrocardiograms for all ages

Reference 38

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Observation e892ff12-ccc5-4a86-8983-13412455f7b5 · outbound

This paper cites Attention is all you need.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Attention is all you need

Reference 39

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Observation 3658ee46-f225-4212-9e38-00df8b02aeae · outbound

This paper cites Ptb-xl, a large publicly available electrocardiog- raphy dataset.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Ptb-xl, a large publicly available electrocardiog- raphy dataset

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:37:28.408414Z

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Observation c8c7e574-74c6-476b-9323-3b669daafa9c · outbound

This paper cites St++: Make self-training work better for semi-supervised se- mantic segmentation.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation St++: Make self-training work better for semi-supervised se- mantic segmentation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:37:28.366682Z

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Observation eacc4921-5ca1-46f2-8ee7-490fc89f8d54 · outbound

This paper cites Augmentation matters: A simple-yet- effective approach to semi-supervised semantic segmenta- tion.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Augmentation matters: A simple-yet- effective approach to semi-supervised semantic segmenta- tion

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:37:28.329320Z

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Observation 55b7407a-0397-481a-a62f-2cd80f4b8d49 · outbound

This paper cites A 12-lead ecg database to identify ori- gins of idiopathic ventricular arrhythmia containing 334 pa- tients.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation A 12-lead ecg database to identify ori- gins of idiopathic ventricular arrhythmia containing 334 pa- tients

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:37:28.302609Z

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Observation c88648a3-4654-4193-aea2-19a56dc30705 · outbound

This paper cites Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers.

SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation Rethinking semantic segmen- tation from a sequence-to-sequence perspective with trans- formers

Reference 44

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