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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:

Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging Data
Mohamed A. Naser, Kareem A. Wahid, Abdallah Mohamed, et al.
Lecture notes in computer science (2022), pp. 287-299
Open Access | Times Cited: 17

Showing 17 citing articles:

Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images
Vincent Andrearczyk, Valentin Oreiller, Sarah Boughdad, et al.
Lecture notes in computer science (2022), pp. 1-37
Closed Access | Times Cited: 80
Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge
Vincent Andrearczyk, Valentin Oreiller, Sarah Boughdad, et al.
Medical Image Analysis (2023) Vol. 90, pp. 102972-102972
Open Access | Times Cited: 17
Artificial Intelligence for Radiation Oncology Applications Using Public Datasets
Kareem A. Wahid, Enrico Glerean, Jaakko Sahlsten, et al.
Seminars in Radiation Oncology (2022) Vol. 32, Iss. 4, pp. 400-414
Open Access | Times Cited: 22
Simplicity Is All You Need: Out-of-the-Box nnUNet Followed by Binary-Weighted Radiomic Model for Segmentation and Outcome Prediction in Head and Neck PET/CT
Louis Rebaud, Thibault Escobar, Fahad Khalid, et al.
Lecture notes in computer science (2023), pp. 121-134
Closed Access | Times Cited: 12
Longitudinal and Multimodal Radiomics Models for Head and Neck Cancer Outcome Prediction
Sebastian Starke, Alex Zwanenburg, Karoline Leger, et al.
Cancers (2023) Vol. 15, Iss. 3, pp. 673-673
Open Access | Times Cited: 11
Role of PET/CT in Oropharyngeal Cancers
Emily W. Avery, Kavita Joshi, Saral Mehra, et al.
Cancers (2023) Vol. 15, Iss. 9, pp. 2651-2651
Open Access | Times Cited: 9
Radiomics prognostic analysis of PET/CT images in a multicenter head and neck cancer cohort: investigating ComBat strategies, sub-volume characterization, and automatic segmentation
Hui Xu, Nassib Abdallah, Jean-Marie Marion, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2023) Vol. 50, Iss. 6, pp. 1720-1734
Closed Access | Times Cited: 8
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer
Mingyuan Meng, Lei Bi, Michael Fulham, et al.
Lecture notes in computer science (2023), pp. 400-410
Closed Access | Times Cited: 7
Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)
Amirhosein Toosi, Isaac Shiri, Habib Zaidi, et al.
Cancers (2024) Vol. 16, Iss. 14, pp. 2538-2538
Open Access | Times Cited: 2
Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma
Kareem A. Wahid, Renjie He, Cem Dede, et al.
Lecture notes in computer science (2022), pp. 300-307
Open Access | Times Cited: 9
Adaptive segmentation-to-survival learning for survival prediction from multi-modality medical images
Mingyuan Meng, Bingxin Gu, Michael Fulham, et al.
npj Precision Oncology (2024) Vol. 8, Iss. 1
Open Access | Times Cited: 1
Multi-Modal Ensemble Deep Learning in Head and Neck Cancer HPV Sub-Typing
Manob Jyoti Saikia, Shiba Kuanar, Dwarikanath Mahapatra, et al.
Bioengineering (2023) Vol. 11, Iss. 1, pp. 13-13
Open Access | Times Cited: 3
Multi-Modal Intermediate Feature Interaction Autoencoder for Overall Survival Prediction of Esophageal Squamous Cell Cancer
Chengyu Wu, Yatao Zhang, Yaqi Wang, et al.
(2024), pp. 1-5
Open Access
Interpretable survival network for progression risk analysis of multimodality imaging biomarkers in poor-prognosis head and neck cancers
Lise Wei, Madhava Aryal, Choonik Lee, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
Deep Learning-Based Outcome Prediction Using Harmonized CT Images for Head and Neck Cancer Patients: A Novel Approach with Attention and DeepHitSingle
Nada Abdallah, Jean-Marie Marion, Clovis Tauber, et al.
(2023), pp. 1-2
Closed Access
Deep-Learning-Based Generation of Synthetic High-Resolution MRI from Low-Resolution MRI for Use in Head and Neck Cancer Adaptive Radiotherapy
Kareem A. Wahid, Jiaofeng Xu, Dina El-Habashy, et al.
medRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access
Deep-learning-based generation of synthetic 6-minute MRI from 2-minute MRI for use in head and neck cancer radiotherapy
Kareem A. Wahid, Jiaofeng Xu, Dina El-Habashy, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access
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