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:

Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma
Amy J. Weisman, Minnie Kieler, Scott B. Perlman, et al.
Radiology Artificial Intelligence (2020) Vol. 2, Iss. 5, pp. e200016-e200016
Open Access | Times Cited: 53

Showing 1-25 of 53 citing articles:

A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging
Tyler Bradshaw, Zachary Huemann, Junjie Hu, et al.
Radiology Artificial Intelligence (2023) Vol. 5, Iss. 4
Open Access | Times Cited: 63
TMTV-Net: fully automated total metabolic tumor volume segmentation in lymphoma PET/CT images — a multi-center generalizability analysis
Fereshteh Yousefirizi, Ivan S. Klyuzhin, Joo Hyun O, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2024) Vol. 51, Iss. 7, pp. 1937-1954
Closed Access | Times Cited: 21
Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development
Tyler Bradshaw, Ronald Boellaard, Joyita Dutta, et al.
Journal of Nuclear Medicine (2021) Vol. 63, Iss. 4, pp. 500-510
Open Access | Times Cited: 69
Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis
Anying Bai, Mingyu Si, Peng Xue, et al.
BMC Medical Informatics and Decision Making (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 14
Image enhancement of whole-body oncology [18F]-FDG PET scans using deep neural networks to reduce noise
Abolfazl Mehranian, Scott D. Wollenweber, Matthew Walker, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2021) Vol. 49, Iss. 2, pp. 539-549
Open Access | Times Cited: 47
AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging
Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar, et al.
PET Clinics (2021) Vol. 17, Iss. 1, pp. 183-212
Closed Access | Times Cited: 43
An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients
Maria C. Ferrández, Sandeep S.V. Golla, Jakoba J. Eertink, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 17
Automated Lugano Metabolic Response Assessment in 18 F-Fluorodeoxyglucose–Avid Non-Hodgkin Lymphoma With Deep Learning on 18 F-Fluorodeoxyglucose–Positron Emission Tomography
Skander Jemaa, Souhila Ounadjela, Xiaoyong Wang, et al.
Journal of Clinical Oncology (2024) Vol. 42, Iss. 25, pp. 2966-2977
Open Access | Times Cited: 8
Quantitative PET-based biomarkers in lymphoma: getting ready for primetime
Juan Pablo Alderuccio, Russ Kuker, Fei Yang, et al.
Nature Reviews Clinical Oncology (2023) Vol. 20, Iss. 9, pp. 640-657
Closed Access | Times Cited: 16
Role of Artificial Intelligence in PET/CT Imaging for Management of Lymphoma
Eren M. Veziroglu, Faraz Farhadi, Navid Hasani, et al.
Seminars in Nuclear Medicine (2023) Vol. 53, Iss. 3, pp. 426-448
Closed Access | Times Cited: 15
Automated quantification of baseline imaging PET metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients
Amy J. Weisman, Ji‐Hyun Kim, Inki Lee, et al.
EJNMMI Physics (2020) Vol. 7, Iss. 1
Open Access | Times Cited: 36
Toward High-Throughput Artificial Intelligence-Based Segmentation in Oncological PET Imaging
Fereshteh Yousefirizi, Abhinav K. Jha, Julia Brosch-Lenz, et al.
PET Clinics (2021) Vol. 16, Iss. 4, pp. 577-596
Open Access | Times Cited: 31
An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients
D.I. Wallis, Michaël Soussan, Maxime Lacroix, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2021) Vol. 49, Iss. 3, pp. 881-888
Open Access | Times Cited: 29
Artificial Intelligence in Lymphoma PET Imaging
Navid Hasani, Sriram S. Paravastu, Faraz Farhadi, et al.
PET Clinics (2021) Vol. 17, Iss. 1, pp. 145-174
Open Access | Times Cited: 28
Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma
Catharina Silvia Lisson, Christoph Gerhard Lisson, Marc Fabian Mezger, et al.
Cancers (2022) Vol. 14, Iss. 8, pp. 2008-2008
Open Access | Times Cited: 22
Combining natural and artificial intelligence for robust automatic anatomy segmentation: Application in neck and thorax auto‐contouring
Jayaram K. Udupa, Tiange Liu, Chao Jin, et al.
Medical Physics (2022) Vol. 49, Iss. 11, pp. 7118-7149
Open Access | Times Cited: 20
Reply to: Automatic for the People: What Has Machine Learning Analysis of Positron Emission Tomography Left for the Physician?
Skander Jemaa, Thomas Bengtsson, Richard A.D. Carano
Journal of Clinical Oncology (2025)
Closed Access
Full-Body Tumor Response Heterogeneity of Metastatic Neuroendocrine Tumor Patients Undergoing Peptide Receptor Radiopharmaceutical Therapy
Victor Santoro-Fernandes, Brayden Schott, Amy J. Weisman, et al.
Journal of Nuclear Medicine (2025), pp. jnumed.124.267809-jnumed.124.267809
Closed Access
Comparative analysis of intestinal tumor segmentation in PET CT scans using organ based and whole body deep learning
Mahsa Torkaman, Skander Jemaa, Jill Fredrickson, et al.
BMC Medical Imaging (2025) Vol. 25, Iss. 1
Open Access
Neural Networks Architectures Design, and Applications: A Review
Mohammed A. M. Sadeeq, Adnan Mohsin Abdulazeez
(2020), pp. 199-204
Closed Access | Times Cited: 30
Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments
Skander Jemaa, Joseph N. Paulson, Martin Hutchings, et al.
Cancer Imaging (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 17
Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability
Daniel T Huff, Victor Santoro-Fernandes, Song Chen, et al.
Physics in Medicine and Biology (2023) Vol. 68, Iss. 17, pp. 175031-175031
Open Access | Times Cited: 9
Comparison of 11 automated PET segmentation methods in lymphoma
Amy J. Weisman, Minnie Kieler, Scott B. Perlman, et al.
Physics in Medicine and Biology (2020) Vol. 65, Iss. 23, pp. 235019-235019
Closed Access | Times Cited: 24
Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography
Jeeone Park, Jihoon Kweon, Young In Kim, et al.
Medical Physics (2023) Vol. 50, Iss. 12, pp. 7822-7839
Open Access | Times Cited: 9
Deep learning for [18F]fluorodeoxyglucose-PET-CT classification in patients with lymphoma: a dual-centre retrospective analysis
Ida Häggström, Doris Leithner, Jennifer Alvén, et al.
The Lancet Digital Health (2023) Vol. 6, Iss. 2, pp. e114-e125
Open Access | Times Cited: 9
Page 1 - Next Page

Scroll to top