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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:

Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, et al.
Medical Image Analysis (2020) Vol. 63, pp. 101693-101693
Open Access | Times Cited: 719

Showing 1-25 of 719 citing articles:

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, et al.
IEEE Transactions on Medical Imaging (2019) Vol. 39, Iss. 6, pp. 1856-1867
Open Access | Times Cited: 2776
Image Segmentation Using Deep Learning: A Survey
Shervin Minaee, Yuri Boykov, Fatih Porikli, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2021), pp. 1-1
Open Access | Times Cited: 2712
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
Deng-Ping Fan, Tao Zhou, Ge-Peng Ji, et al.
IEEE Transactions on Medical Imaging (2020) Vol. 39, Iss. 8, pp. 2626-2637
Open Access | Times Cited: 1077
Deep semantic segmentation of natural and medical images: a review
Saeid Asgari Taghanaki, Kumar Abhishek, Joseph Cohen, et al.
Artificial Intelligence Review (2020) Vol. 54, Iss. 1, pp. 137-178
Closed Access | Times Cited: 684
A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises
S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, et al.
Proceedings of the IEEE (2021) Vol. 109, Iss. 5, pp. 820-838
Open Access | Times Cited: 683
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
Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
Davood Karimi, Haoran Dou, Simon K. Warfield, et al.
Medical Image Analysis (2020) Vol. 65, pp. 101759-101759
Open Access | Times Cited: 521
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey
Sweta Bhattacharya, Praveen Kumar Reddy Maddikunta, Quoc‐Viet Pham, et al.
Sustainable Cities and Society (2020) Vol. 65, pp. 102589-102589
Open Access | Times Cited: 435
A survey on active learning and human-in-the-loop deep learning for medical image analysis
Samuel Budd, Emma C. Robinson, Bernhard Kainz
Medical Image Analysis (2021) Vol. 71, pp. 102062-102062
Open Access | Times Cited: 430
Medical image segmentation using deep learning: A survey
Risheng Wang, Tao Lei, Ruixia Cui, et al.
IET Image Processing (2022) Vol. 16, Iss. 5, pp. 1243-1267
Open Access | Times Cited: 422
UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation
Yunhe Gao, Mu Zhou, Dimitris Metaxas
Lecture notes in computer science (2021), pp. 61-71
Closed Access | Times Cited: 330
AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?
Jun Ma, Yao Zhang, Song Gu, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) Vol. 44, Iss. 10, pp. 6695-6714
Open Access | Times Cited: 239
COVID-19 open source data sets: a comprehensive survey
Junaid Shuja, Eisa Alanazi, Waleed Alasmary, et al.
Applied Intelligence (2020) Vol. 51, Iss. 3, pp. 1296-1325
Open Access | Times Cited: 209
Deep learning for biomedical photoacoustic imaging: A review
Janek Gröhl, Melanie Schellenberg, Kris K. Dreher, et al.
Photoacoustics (2021) Vol. 22, pp. 100241-100241
Open Access | Times Cited: 190
Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images
Naveen Paluru, Aveen Dayal, Håvard Bjørke Jenssen, et al.
IEEE Transactions on Neural Networks and Learning Systems (2021) Vol. 32, Iss. 3, pp. 932-946
Open Access | Times Cited: 152
Self-supervised learning methods and applications in medical imaging analysis: a survey
Saeed Shurrab, Rehab Duwairi
PeerJ Computer Science (2022) Vol. 8, pp. e1045-e1045
Open Access | Times Cited: 149
Vision Transformers in medical computer vision—A contemplative retrospection
Arshi Parvaiz, Muhammad Anwaar Khalid, Rukhsana Zafar, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 122, pp. 106126-106126
Open Access | Times Cited: 145
Annotation-efficient deep learning for automatic medical image segmentation
Shanshan Wang, Cheng Li, Rongpin Wang, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 144
Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends
Imran Qureshi, Junhua Yan, Qaisar Abbas, et al.
Information Fusion (2022) Vol. 90, pp. 316-352
Closed Access | Times Cited: 140
Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images
Xiaoming Liu, Quan Yuan, Yaozong Gao, et al.
Pattern Recognition (2021) Vol. 122, pp. 108341-108341
Open Access | Times Cited: 131
BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset
Alberto Signoroni, Mattia Savardi, Sergio Benini, et al.
Medical Image Analysis (2021) Vol. 71, pp. 102046-102046
Open Access | Times Cited: 124
Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging
Gelan Ayana, Kokeb Dese, Se‐woon Choe
Cancers (2021) Vol. 13, Iss. 4, pp. 738-738
Open Access | Times Cited: 123
Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation
Rushi Jiao, Yichi Zhang, Le Ding, et al.
Computers in Biology and Medicine (2023) Vol. 169, pp. 107840-107840
Open Access | Times Cited: 120
Medical image analysis using deep learning algorithms
Mengfang Li, Yuanyuan Jiang, Yanzhou Zhang, et al.
Frontiers in Public Health (2023) Vol. 11
Open Access | Times Cited: 115
Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation
Qi Chen, Lingxiao Yang, Jianhuang Lai, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 4278-4288
Open Access | Times Cited: 114
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