activity
20202022
most citedDeep Expectation-Maximization for Semi-Supervised Lung Cancer Screening

9 citations · 19 across the 8 of their papers we have counts for

collaborators

8 papers

physics.med-ph20222 cited

Issues and Challenges in Applications of Artificial Intelligence to Nuclear Medicine -- The Bethesda Report (AI Summit 2022)

Arman Rahmim, Tyler J. Bradshaw, Irène Buvat +10

The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community me…

eess.IV2021

CCS-GAN: COVID-19 CT-scan classification with very few positive training images

Sumeet Menon, Jayalakshmi Mangalagiri, Josh Galita +7

We present a novel algorithm that is able to classify COVID-19 pneumonia from CT Scan slices using a very small sample of training images exhibiting COVID-19 pneumonia in tandem wi…

physics.med-ph20211 cited

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…

physics.med-ph20217 cited

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…

cs.CV2021

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…

eess.IV2021

Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network

Jayalakshmi Mangalagiri, David Chapman, Aryya Gangopadhyay +7

We present a novel conditional Generative Adversarial Network (cGAN) architecture that is capable of generating 3D Computed Tomography scans in voxels from noisy and/or pixelated a…