7 citations · 8 across the 3 of their papers we have counts for
10 papers
3-D PET Image Generation with tumour masks using TGAN
Robert V Bergen, Jean-Francois Rajotte, Fereshteh Yousefirizi +3
Training computer-vision related algorithms on medical images for disease diagnosis or image segmentation is difficult due to the lack of training data, labeled samples, and privac…
Role of AI in Theranostics: Towards Routine Personalized Radiopharmaceutical Therapies
Julia Brosch-Lenz, Fereshteh Yousefirizi, Katherine Zukotynski +5
We highlight emerging uses of artificial intelligence (AI) in the field of theranostics, focusing on its significant potential to enable routine and reliable personalization of rad…
Objective task-based evaluation of artificial intelligence-based medical imaging methods: Framework, strategies and role of the physician
Abhinav K. Jha, Kyle J. Myers, Nancy A. Obuchowski +5
Artificial intelligence (AI)-based methods are showing promise in multiple medical-imaging applications. Thus, there is substantial interest in clinical translation of these method…
Artificial Intelligence in PET: an Industry Perspective
Arkadiusz Sitek, Sangtae Ahn, Evren Asma +6
Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has t…
A Physics-Guided Modular Deep-Learning Based Automated Framework for Tumor Segmentation in PET Images
Kevin H. Leung, Wael Marashdeh, Rick Wray +4
The objective of this study was to develop a PET tumor-segmentation framework that addresses the challenges of limited spatial resolution, high image noise, and lack of clinical tr…
Next Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Approaches
Isaac Shiri, Hassan Maleki, Ghasem Hajianfar +5
Aim: In the present work, we aimed to evaluate a comprehensive radiomics framework that enabled prediction of EGFR and KRAS mutation status in NSCLC cancer patients based on PET an…