Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Showing 10 citing articles:

Subspace learning for feature selection via rank revealing QR factorization: Fast feature selection
Amir Moslemi, Arash Ahmadian
Expert Systems with Applications (2024) Vol. 256, pp. 124919-124919
Closed Access | Times Cited: 4
Deep Texture Analysis—Enhancing CT Radiomics Features for Prediction of Head and Neck Cancer Treatment Outcomes: A Machine Learning Approach
Aryan Safakish, Lakshmanan Sannachi, Amir Moslemi, et al.
Radiation (2024) Vol. 4, Iss. 1, pp. 50-68
Open Access | Times Cited: 2
Apriori prediction of chemotherapy response in locally advanced breast cancer patients using CT imaging and deep learning: transformer versus transfer learning
Amir Moslemi, Laurentius O. Osapoetra, Archya Dasgupta, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 2
Quantitative ultrasound radiomics analysis to evaluate lymph nodes in patients with cancer: a systematic review
Antonio Guerrisi, Ludovica Miseo, Italia Falcone, et al.
Ultraschall in der Medizin - European Journal of Ultrasound (2024)
Closed Access
Predicting Head & Neck Cancer Treatment Outcomes with Quantitative Ultrasound Texture Features & Optimizing Machine Learning Classifiers with Novel Texture-of-Texture Features
Aryan Safakish, Lakshmanan Sannachi, Daniel DiCenzo, et al.
(2024)
Open Access
Predicting Head & Neck Cancer Treatment Outcomes with Quantitative Ultrasound Texture Features & Optimizing Machine Learning Classifiers with Novel Texture-of-Texture Features
Aryan Safakish, Lakshmanan Sannachi, Daniel DiCenzo, et al.
(2024)
Open Access
Enhancing CT Radiomics Features for Prediction of Head and Neck Cancer Treatment Outcomes: A Machine Learning Approach
Aryan Safakish, Lakshmanan Sannachi, Amir Moslemi, et al.
(2024)
Open Access
Enhancing CT Radiomics Features for Prediction of Head and Neck Cancer Treatment Outcomes: A Machine Learning Approach
Aryan Safakish, Lakshmanan Sannachi, Amir Moslemi, et al.
(2024)
Open Access
Deep Texture Analysis Enhanced MRI Radiomics for Predicting Head and Neck Cancer Treatment Outcomes with Machine Learning Classifiers
Aryan Safakish, Amir Moslemi, Daniel Moore-Palhares, et al.
Radiation (2024) Vol. 4, Iss. 2, pp. 192-212
Open Access
Improved Cervical Lymph Node Characterization among Patients with Head and Neck Squamous Cell Carcinoma Using MR Texture Analysis Compared to Traditional FDG-PET/MR Features Alone
Eric K. van Staalduinen, Robert Matthews, Adam Z. Khan, et al.
Diagnostics (2023) Vol. 14, Iss. 1, pp. 71-71
Open Access | Times Cited: 1
Page 1

Scroll to top