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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

3D Self-Supervised Methods for Medical Imaging
Aiham Taleb, Winfried Loetzsch, Noel Danz, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 118

Showing 1-25 of 118 citing articles:

Recent advances and clinical applications of deep learning in medical image analysis
Xuxin Chen, Ximin Wang, Ke Zhang, et al.
Medical Image Analysis (2022) Vol. 79, pp. 102444-102444
Open Access | Times Cited: 538
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
Yucheng Tang, Dong Yang, Wenqi Li, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 20698-20708
Open Access | Times Cited: 427
Self-Supervised Representation Learning: Introduction, advances, and challenges
Linus Ericsson, Henry Gouk, Chen Change Loy, et al.
IEEE Signal Processing Magazine (2022) Vol. 39, Iss. 3, pp. 42-62
Open Access | Times Cited: 252
SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation
Chenyu You, Yuan Zhou, Ruihan Zhao, et al.
IEEE Transactions on Medical Imaging (2022) Vol. 41, Iss. 9, pp. 2228-2237
Open Access | Times Cited: 207
Models Genesis
Zongwei Zhou, Vatsal Sodha, Jiaxuan Pang, et al.
Medical Image Analysis (2020) Vol. 67, pp. 101840-101840
Open Access | Times Cited: 203
Current and Emerging Trends in Medical Image Segmentation With Deep Learning
Pierre-Henri Conze, Gustavo Andrade-Miranda, Vivek Kumar Singh, et al.
IEEE Transactions on Radiation and Plasma Medical Sciences (2023) Vol. 7, Iss. 6, pp. 545-569
Open Access | Times Cited: 53
Masked Image Modeling Advances 3D Medical Image Analysis
Zekai Chen, Devansh Agarwal, K.K. Aggarwal, et al.
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2023), pp. 1969-1979
Open Access | Times Cited: 43
Analysis of 3D pathology samples using weakly supervised AI
Andrew H. Song, Mane Williams, Drew F. K. Williamson, et al.
Cell (2024) Vol. 187, Iss. 10, pp. 2502-2520.e17
Open Access | Times Cited: 24
A comprehensive survey on deep active learning in medical image analysis
Haoran Wang, Qiuye Jin, Shiman Li, et al.
Medical Image Analysis (2024) Vol. 95, pp. 103201-103201
Open Access | Times Cited: 16
Rotation-Oriented Collaborative Self-Supervised Learning for Retinal Disease Diagnosis
Xiaomeng Li, Xiaowei Hu, Xiaojuan Qi, et al.
IEEE Transactions on Medical Imaging (2021) Vol. 40, Iss. 9, pp. 2284-2294
Closed Access | Times Cited: 75
Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts
Hong-Yu Zhou, Chixiang Lu, Sibei Yang, et al.
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2021)
Open Access | Times Cited: 64
Medical Image Segmentation With Limited Supervision: A Review of Deep Network Models
Jialin Peng, Ye Wang
IEEE Access (2021) Vol. 9, pp. 36827-36851
Open Access | Times Cited: 60
DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis
Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 20792-20802
Open Access | Times Cited: 59
Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency
Ana María Barragán Montero, Adrien Bibal, Margerie Huet Dastarac, et al.
Physics in Medicine and Biology (2022) Vol. 67, Iss. 11, pp. 11TR01-11TR01
Open Access | Times Cited: 49
Dive into the details of self-supervised learning for medical image analysis
Chuyan Zhang, Hao Zheng, Yun Gu
Medical Image Analysis (2023) Vol. 89, pp. 102879-102879
Closed Access | Times Cited: 36
A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis
Hong-Yu Zhou, Chixiang Lu, Chaoqi Chen, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2023), pp. 1-16
Closed Access | Times Cited: 31
Self-supervised spatial–temporal transformer fusion based federated framework for 4D cardiovascular image segmentation
Moona Mazher, Imran Razzak, Abdul Qayyum, et al.
Information Fusion (2024) Vol. 106, pp. 102256-102256
Open Access | Times Cited: 12
Self-supervised learning for medical image analysis: Discriminative, restorative, or adversarial?
Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway, et al.
Medical Image Analysis (2024) Vol. 94, pp. 103086-103086
Closed Access | Times Cited: 8
A Review of Self-supervised Learning Methods in the Field of Medical Image Analysis
Jiashu Xu
International Journal of Image Graphics and Signal Processing (2021) Vol. 13, Iss. 4, pp. 33-46
Open Access | Times Cited: 41
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics
Aiham Taleb, Matthias Kirchler, Remo Monti, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 20876-20889
Open Access | Times Cited: 33
PRIOR: Prototype Representation Joint Learning from Medical Images and Reports
Pujin Cheng, Li Lin, Junyan Lyu, et al.
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2023), pp. 21304-21314
Open Access | Times Cited: 21
Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data
Paul Hager, Martin J. Menten, Daniel Rueckert
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
Open Access | Times Cited: 20
Heterogeneous Graph Learning for Multi-Modal Medical Data Analysis
Sein Kim, Namkyeong Lee, Junseok Lee, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2023) Vol. 37, Iss. 4, pp. 5141-5150
Open Access | Times Cited: 19
Self-supervised contrastive learning with random walks for medical image segmentation with limited annotations
Marc Fischer, Tobias Hepp, Sergios Gatidis, et al.
Computerized Medical Imaging and Graphics (2023) Vol. 104, pp. 102174-102174
Closed Access | Times Cited: 17
M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities
Hong Liu, Dong Wei, Donghuan Lu, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2023) Vol. 37, Iss. 2, pp. 1657-1665
Open Access | Times Cited: 17
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