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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:

Detection of Bone Metastases on Bone Scans through Image Classification with Contrastive Learning
Te‐Chun Hsieh, Chiung-Wei Liao, Yung-Chi Lai, et al.
Journal of Personalized Medicine (2021) Vol. 11, Iss. 12, pp. 1248-1248
Open Access | Times Cited: 23

Showing 23 citing articles:

Enhanced Bone Cancer Diagnosis through Deep Learning on Medical Imagery
M. Venkata Ramana, P. N. Siva Jyothi, S. G. Anuradha, et al.
International Journal of Computational and Experimental Science and Engineering (2025) Vol. 11, Iss. 1
Open Access | Times Cited: 1
Advancements in the development of radiopharmaceuticals for nuclear medicine applications in the treatment of bone metastases
Michael R. Dyer, Zhenghan Jing, Kathleen Duncan, et al.
Nuclear Medicine and Biology (2024) Vol. 130-131, pp. 108879-108879
Closed Access | Times Cited: 7
Deep Dive into Bone Tumor Segmentation and Classification: Methodological Review and Challenges with Deep Learning Approaches
R Singh, D Vasumathi
ITM Web of Conferences (2025) Vol. 74, pp. 01006-01006
Open Access
Bone Metastasis in Prostate Cancer: Bone Scan Versus PET Imaging
Nasibeh Mohseninia, Nazanin Zamani-Siahkali, Sara Harsini, et al.
Seminars in Nuclear Medicine (2023) Vol. 54, Iss. 1, pp. 97-118
Open Access | Times Cited: 15
Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance
Ghasem Hajianfar, Maziar Sabouri, Yazdan Salimi, et al.
Zeitschrift für Medizinische Physik (2023) Vol. 34, Iss. 2, pp. 242-257
Open Access | Times Cited: 13
From Segmentation to Biomarker Quantification: A Deep Learning Framework for Metastases Detection in Bone Scans
Ema Rachmawati, M. Rifqi Dzaky Azhad, Ida Bagus Indrabudhi Kusuma, et al.
(2025)
Closed Access
Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications
Maxime Lacroix, Théodore Aouad, Jean Feydy, et al.
Diagnostic and Interventional Imaging (2022) Vol. 104, Iss. 1, pp. 18-23
Open Access | Times Cited: 18
Artificial Intelligence of Object Detection in Skeletal Scintigraphy for Automatic Detection and Annotation of Bone Metastases
Chiung-Wei Liao, Te‐Chun Hsieh, Yung-Chi Lai, et al.
Diagnostics (2023) Vol. 13, Iss. 4, pp. 685-685
Open Access | Times Cited: 7
Systematic Review of Tumor Segmentation Strategies for Bone Metastases
Iromi R. Paranavithana, David Stirling, Montserrat Ros, et al.
Cancers (2023) Vol. 15, Iss. 6, pp. 1750-1750
Open Access | Times Cited: 7
ChatGPT-assisted deep learning for diagnosing bone metastasis in bone scans: Bridging the AI Gap for Clinicians
Hye Joo Son, Soo-Jong Kim, Sehyun Pak, et al.
Heliyon (2023) Vol. 9, Iss. 12, pp. e22409-e22409
Open Access | Times Cited: 6
Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence
Nicholas Moore, A. McWilliam, Sanjay Aneja
Seminars in Radiation Oncology (2022) Vol. 33, Iss. 1, pp. 70-75
Closed Access | Times Cited: 10
Detection of Osseous Metastasis From Bone Scintigrams Using a Combined Global and Local Patch-Based Deep Learning Model
Swailem Neil Angelo Lumba, Emmanuel Linus Evangelista, Kyla Sydney Martin, et al.
(2024), pp. 36-40
Closed Access | Times Cited: 1
Bone scintigraphy based on deep learning model and modified growth optimizer
Omnia Magdy, Mohamed Abd Elaziz, Abdelghani Dahou, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 1
Artificial Intelligence in Musculoskeletal Oncology
Raffaele Vitiello, Antonio Ziranu, Giulio Maccauro
(2024), pp. 173-178
Closed Access | Times Cited: 1
AI‐powered automated analysis of bone scans: A survey
Qiang Lin, Yang He, Sihan Guo
IET Image Processing (2024)
Open Access | Times Cited: 1
A systematic review for using deep learning in bone scan classification
Yung‐Shuo Kao, Chun-Pang Huang, Wen-Wen Tsai, et al.
Clinical and Translational Imaging (2023) Vol. 11, Iss. 3, pp. 271-283
Closed Access | Times Cited: 2
Accuracy of Artificial Intelligence in Detecting Tumor Bone Metastasis: A Systematic Review and Meta-Analysis
Huimin Tao, Hui Xu, Zhi Hong Zhang, et al.
(2024)
Closed Access
Comparison of 68GA-FAPI-04 PET/CT and 18F-FDG PET/CT in detection of metastatic bone disease in various cancers
Ferat Kepenek, İhsan Kaplan, C. Can, et al.
Médecine Nucléaire (2023) Vol. 47, Iss. 4, pp. 200-207
Closed Access | Times Cited: 1
An efficient segmentation technique for skeletal scintigraphy image based on sharpness index and salp swarm algorithm
Mohammed M. Nasef, Fatma T. Eid, Mohammed A. Amin, et al.
Biomedical Signal Processing and Control (2022) Vol. 79, pp. 104046-104046
Closed Access | Times Cited: 2
Bone Tumours - A Comprehensive Review of Selected Topics

IntechOpen eBooks (2022)
Closed Access | Times Cited: 1
Computer-aided diagnosis of skeletal metastases in multi-parametric whole-body MRI
Jakub Ceranka, Joris Wuts, Ophélye Chiabai, et al.
Computer Methods and Programs in Biomedicine (2023) Vol. 242, pp. 107811-107811
Open Access
Explainable Contrastive and Cost-Sensitive Learning for Cervical Cancer Classification
Ashfiqun Mustari, Rushmia Ahmed, Afsara Tasnim, et al.
(2023), pp. 1-6
Open Access
Perspective Chapter: Bone Tumors – How to Make a Diagnosis?
Jairo Greco Garcia
IntechOpen eBooks (2022)
Open Access
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