Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation | Paper Citation Record

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This paper cites Enhancing medical image segmentation: Ground truth optimization through evaluating uncertainty in expert annotations.Mathematics, 11(17):3771, 2023.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Enhancing medical image segmentation: Ground truth optimization through evaluating uncertainty in expert annotations.Mathematics, 11(17):3771, 2023

Reference 1

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This paper cites There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average

Reference 2

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This paper cites Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation

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This paper cites Matthews, and Daniel Rueck- ert.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Matthews, and Daniel Rueck- ert

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This paper cites Albumentations: fast and flexible image augmen- tations.Information, 11(2):125, 2020.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Albumentations: fast and flexible image augmen- tations.Information, 11(2):125, 2020

Reference 5

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This paper cites Hippe, Xihai Zhao, Rui Li, Thomas S.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Hippe, Xihai Zhao, Rui Li, Thomas S

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This paper cites Recent advances and clinical applications of deep learning in medical image analysis.Medical image analysis, 79:102444, 2022.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Recent advances and clinical applications of deep learning in medical image analysis.Medical image analysis, 79:102444, 2022

Reference 7

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Observation f2adf0f9-10b5-4826-af27-31e4162510c0 · outbound

This paper cites Stroke risk study based on deep learning-based magnetic resonance imaging carotid plaque automatic segmentation algorithm.Frontiers in Cardiovascular Medicine, 10, 2023.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Stroke risk study based on deep learning-based magnetic resonance imaging carotid plaque automatic segmentation algorithm.Frontiers in Cardiovascular Medicine, 10, 2023

Reference 8

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This paper cites Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Semi-Supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model

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This paper cites Deep learning technology in vascular image segmentation and disease diagnosis.Journal of Intelligent Medicine, 2024.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Deep learning technology in vascular image segmentation and disease diagnosis.Journal of Intelligent Medicine, 2024

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This paper cites Semi-supervised learning for pelvic mr image segmentation based on multi-task residual fully convolutional net- works.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Semi-supervised learning for pelvic mr image segmentation based on multi-task residual fully convolutional net- works

Reference 11

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This paper cites Semi-supervised learning by entropy minimiza- tion.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Semi-supervised learning by entropy minimiza- tion

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Observation aa4f10bb-dbc8-4c6f-9bd4-31ff305d0d5a · outbound

This paper cites Revisiting consistency for semi- supervised semantic segmentation.Sensors, 23(2):940, 2023.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Revisiting consistency for semi- supervised semantic segmentation.Sensors, 23(2):940, 2023

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This paper cites Sheng, Yuqing Song, Yi Liu, Chengjian Qiu, Siqi Ma, and Zhe Liu.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Sheng, Yuqing Song, Yi Liu, Chengjian Qiu, Siqi Ma, and Zhe Liu

Reference 15

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This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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This paper cites Squeeze-and-Excitation Networks.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Squeeze-and-Excitation Networks

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This paper cites J¨ ager, Simon A.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation J¨ ager, Simon A

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This paper cites Averaging Weights Leads to Wider Optima and Better Generalization.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Averaging Weights Leads to Wider Optima and Better Generalization

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This paper cites Deep learning applications in medical image analysis.Ieee Access, 6:9375–9389, 2017.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Deep learning applications in medical image analysis.Ieee Access, 6:9375–9389, 2017

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This paper cites Comparative review on traditional and deep learning methods for medical image segmentation.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Comparative review on traditional and deep learning methods for medical image segmentation

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This paper cites Londhe, S.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Londhe, S

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Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Unresolved cited work

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Observation ffed0452-d068-441d-9dce-4ae3b66b53d1 · outbound

This paper cites Transformation-consistent self-ensembling model for semisupervised medical image seg- mentation.IEEE transactions on neural networks and learning systems, 32(2):523–534, 2020.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Transformation-consistent self-ensembling model for semisupervised medical image seg- mentation.IEEE transactions on neural networks and learning systems, 32(2):523–534, 2020

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This paper cites A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities.Healthcare Analytics, page 100216, 2023.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities.Healthcare Analytics, page 100216, 2023

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This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Attention U-Net: Learning Where to Look for the Pancreas

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This paper cites Synthetic ground truth for validation of brain tumor mri segmentation.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Synthetic ground truth for validation of brain tumor mri segmentation

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Observation 3c8c2950-253c-4fe4-ad8c-ec89848bfd7e · outbound

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Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Semi-supervised segmentation of retinoblastoma tumors in fundus images

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Observation 9c41209f-aab1-4a78-8df1-70eda51964f0 · outbound

This paper cites You Only Look Once: Unified, Real-Time Object Detection.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation You Only Look Once: Unified, Real-Time Object Detection

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Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation U-Net: Convolutional Networks for Biomedical Image Segmentation

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This paper cites Automated medical image segmentation techniques.Journal of Medical Physics / Association of Medical Physicists of India, 35:3 – 14, 2010.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Automated medical image segmentation techniques.Journal of Medical Physics / Association of Medical Physicists of India, 35:3 – 14, 2010

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This paper cites Mean teachers are better role models: Weight- averaged consistency targets improve semi-supervised deep learning results.Advances in neural information processing systems, 30, 2017.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Mean teachers are better role models: Weight- averaged consistency targets improve semi-supervised deep learning results.Advances in neural information processing systems, 30, 2017

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This paper cites Tsakanikas, Panagiotis K.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Tsakanikas, Panagiotis K

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Observation f67780c0-a05a-4ef4-8aaf-887cab6d815c · outbound

This paper cites Understanding interobserver agreement: The kappa statistic.Family medicine, 37:360–3, 06 2005.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Understanding interobserver agreement: The kappa statistic.Family medicine, 37:360–3, 06 2005

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Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Unresolved cited work

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Observation 6be4a221-3002-4713-8b56-4b6d94e99c54 · outbound

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Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Understanding Convolution for Semantic Segmentation

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This paper cites Application of artificial intelligence methods in carotid artery segmentation: a review.IEEE Access, 2023.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Application of artificial intelligence methods in carotid artery segmentation: a review.IEEE Access, 2023

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This paper cites Simultaneous truth and perfor- mance level estimation (staple): an algorithm for the validation of image segmentation.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Simultaneous truth and perfor- mance level estimation (staple): an algorithm for the validation of image segmentation

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Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Unresolved cited work

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Observation fe23e0ed-13d4-4b90-952f-128a4e20e3aa · outbound

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Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation A comprehensive review of deep learning for medical image segmentation

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Observation 063687ee-8beb-4d01-bcc4-a1ae658cfa5c · outbound

This paper cites A semantic segmentation method with emphasis on the edges for automatic vessel wall analysis.Applied Sciences, 12(14), 2022.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation A semantic segmentation method with emphasis on the edges for automatic vessel wall analysis.Applied Sciences, 12(14), 2022

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Observation 5c0582a2-8d98-416b-b2e5-c20b58f615c1 · outbound

This paper cites Deep learning- based automated detection of arterial vessel wall and plaque on magnetic resonance vessel wall images.Frontiers in Neuroscience, 16, June 2022.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Deep learning- based automated detection of arterial vessel wall and plaque on magnetic resonance vessel wall images.Frontiers in Neuroscience, 16, June 2022

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Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Unresolved cited work

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Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmenta- tion

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Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Road Extraction by Deep Residual U-Net

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Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Unresolved cited work

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Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Unresolved cited work

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This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation Rich feature hierarchies for accurate object detection and semantic segmentation

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This paper cites doi: 10.1109/EMBC44109.2020.9176532.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation doi: 10.1109/EMBC44109.2020.9176532

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This paper cites URLhttps://www.mdpi.com/2076-3417/12/14/ 7012.

Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation URLhttps://www.mdpi.com/2076-3417/12/14/ 7012

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