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:

An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1‐weighted images
Yida Wang, Naying He, Chunyan Zhang, et al.
Human Brain Mapping (2023) Vol. 44, Iss. 12, pp. 4426-4438
Open Access | Times Cited: 14

Showing 14 citing articles:

Neuroimaging and fluid biomarkers in Parkinson’s disease in an era of targeted interventions
Angeliki Zarkali, George E. Thomas, Henrik Zetterberg, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 12

Automated machine learning with interpretation: A systematic review of methodologies and applications in healthcare
Han Yuan, Kunyu Yu, Feng Xie, et al.
Medicine Advances (2024) Vol. 2, Iss. 3, pp. 205-237
Open Access | Times Cited: 11

PIDGN: An explainable multimodal deep learning framework for early prediction of Parkinson's disease
Wenjia Li, Qiu Rao, Shuying Dong, et al.
Journal of Neuroscience Methods (2025), pp. 110363-110363
Closed Access | Times Cited: 1

A review of machine learning and deep learning for Parkinson’s disease detection
Helena Rabie, Moulay A. Akhloufi
Discover Artificial Intelligence (2025) Vol. 5, Iss. 1
Open Access

Classification of Parkinson’s disease by deep learning on midbrain MRI
Thomas Welton, Septian Hartono, Weiling Lee, et al.
Frontiers in Aging Neuroscience (2024) Vol. 16
Open Access | Times Cited: 2

Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review
Mirko Jerber Rodríguez Mallma, Luis Zuloaga-Rotta, Rubén Borja-Rosales, et al.
Neurology International (2024) Vol. 16, Iss. 6, pp. 1285-1307
Open Access | Times Cited: 1

Quantitative susceptibility mapping based basal ganglia segmentation via AGSeg: leveraging active gradient guiding mechanism in deep learning
Jiaxiu Xi, Yuqing Huang, Lijun Bao
Quantitative Imaging in Medicine and Surgery (2024) Vol. 14, Iss. 7, pp. 4417-4435
Open Access

Automatic segmentation model for Parkinson's images based on SA-U2-Net
Hui Li, Zixuan Yang, Weimin Qi, et al.
International Journal of Pattern Recognition and Artificial Intelligence (2024)
Closed Access

DepthParkNet: A 3D Convolutional Neural Network with Depth-Aware Coordinate Attention for PET-Based Parkinson's Disease Diagnosis
Maoyuan Li, Ling Chen, Jianmin Chu, et al.
Lecture notes in computer science (2024), pp. 61-72
Closed Access

Forecast the Onset of Parkinson’s Disease at all Three Stages using Deep Learning Techniques
Caroline El Fiorenza J, V. Sellam, J. Dafni Rose
(2023), pp. 1-8
Closed Access | Times Cited: 1

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