17 citations · 37 across the 24 of their papers we have counts for
25 papers
Rethinking AI in Medical Imaging: Assumptions, Reality, and Reframing; Towards Physician-Centered Agentic Reasoning
Arman Rahmim, Nourhan Bayasi, Xiaoxiao Li +2
Medical imaging has served as primary proving ground for clinical AI, yet a decade of intense research has not translated into proportionate bedside impact. We argue that this gap…
Towards Routine AI-Based PET/CT and SPECT/CT Lesion Segmentation and Tracking in PSMA Theranostics
Fereshteh Yousefirizi, Jean-Mathieu Beauregard, Arman Rahmim
Quantitative molecular imaging is central to treatment response assessment in oncology, yet clinical practice remains largely dominated by patient-level or limited target-lesion cr…
Beyond the Tumor: Recurrence-Prone Radiomics for Prognostication in Negative PSMA PET/CT scans of Prostate Cancer
Fereshteh Yousefirizi, Sara Harsini, Mobin Mohebi +14
In patients with biochemical recurrence of prostate cancer and negative PSMA PET/CT, radiomics features extracted from recurrence-prone organs can predict clinical progression and…
Virtual Theranostic Trials: New Approach Methodologies (NAMs) and Precision Radiopharmaceutical Therapies
Carlos F. Uribe, Hamid Abdollahi, Tahir Yusufaly +7
Radiopharmaceutical therapies are expanding rapidly, but clinical evidence generation is limited by operational constraints and biological heterogeneity in radiopharmaceutical deli…
Towards Integrated Clinical-Computational Nuclear Medicine
Faraz Farhadi, Shadi A. Esfahani, Fereshteh Yousefirizi +7
The field of Clinical-Computational Nuclear Medicine is rapidly advancing, fueled by AI, tracer kinetic modeling, radiomics, and integrated informatics. These technologies improve…
Seeing More, Treating Smarter: Role of Long-Axial Field-of-View PET-CT in The Evolution of Theranostics
Pedro L. Esquinas, Fereshteh Yousefirizi, Ian Alberts +3
Long-axial field-of-view (LAFOV) PET/CT has the potential to redefine the role of molecular imaging in theranostics by making multiparametric whole-body (MPWB) imaging and predicti…
Artificial intelligence for simplified patient-centered dosimetry in radiopharmaceutical therapies
Alejandro Lopez-Montes, Fereshteh Yousefirizi, Yizhou Chen +8
KEY WORDS: Artificial Intelligence (AI), Theranostics, Dosimetry, Radiopharmaceutical Therapy (RPT), Patient-friendly dosimetry KEY POINTS - The rapid evolution of radiopharmaceuti…
Advancing Positron Emission Tomography Image Quantification: Artificial Intelligence-Driven Methods, Clinical Challenges, and Emerging Opportunities in Long-Axial Field-of-View Positron Emission Tomography/Computed Tomography Imaging
Fereshteh Yousefirizi, Movindu Dassanayake, Alejandro Lopez +6
MTV is increasingly recognized as an accurate estimate of disease burden, which has prognostic value, but its implementation has been hindered by the time-consuming need for manual…
Artificial Intelligence-Guided PET Image Reconstruction and Multi-Tracer Imaging: Novel Methods, Challenges, And Opportunities
Movindu Dassanayake, Alejandro Lopez, Andrew Reader +6
LAFOV PET/CT has the potential to unlock new applications such as ultra-low dose PET/CT imaging, multiplexed imaging, for biomarker development and for faster AI-driven reconstruct…
DeepBoost-AF: A Novel Unsupervised Feature Learning and Gradient Boosting Fusion for Robust Atrial Fibrillation Detection in Raw ECG Signals
Alireza Jafari, Fereshteh Yousefirizi, Vahid Seydi
Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with elevated health risks, where timely detection is pivotal for mitigating stroke-related morbidity. This st…
Thyroidiomics: An Automated Pipeline for Segmentation and Classification of Thyroid Pathologies from Scintigraphy Images
Maziar Sabouri, Shadab Ahamed, Azin Asadzadeh +13
The objective of this study was to develop an automated pipeline that enhances thyroid disease classification using thyroid scintigraphy images, aiming to decrease assessment time…
RT-utils: A Minimal Python Library for RT-struct Manipulation
Asim Shrestha, Adam Watkins, Fereshteh Yousefirizi +2
Towards the need for automated and precise AI-based analysis of medical images, we present RT-utils, a specialized Python library tuned for the manipulation of radiotherapy (RT) st…
A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma
Shadab Ahamed, Natalia Dubljevic, Ingrid Bloise +6
Accurate detection and segmentation of diffuse large B-cell lymphoma (DLBCL) from PET images has important implications for estimation of total metabolic tumor volume, radiomics an…
Observer study-based evaluation of TGAN architecture used to generate oncological PET images
Roberto Fedrigo, Fereshteh Yousefirizi, Ziping Liu +9
The application of computer-vision algorithms in medical imaging has increased rapidly in recent years. However, algorithm training is challenging due to limited sample sizes, lack…
Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: clinical insights, pitfalls, and observer agreement analyses
Shadab Ahamed, Yixi Xu, Sara Kurkowska +12
This study addresses critical gaps in automated lymphoma segmentation from PET/CT images, focusing on issues often overlooked in existing literature. While deep learning has been a…
Semi-supervised learning towards automated segmentation of PET images with limited annotations: Application to lymphoma patients
Fereshteh Yousefirizi, Isaac Shiri, Joo Hyun O +9
The time-consuming task of manual segmentation challenges routine systematic quantification of disease burden. Convolutional neural networks (CNNs) hold significant promise to reli…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Convolutional neural network with a hybrid loss function for fully automated segmentation of lymphoma lesions in FDG PET images
Fereshteh Yousefirizi, Natalia Dubljevic, Shadab Ahamed +9
Segmentation of lymphoma lesions is challenging due to their varied sizes and locations in whole-body PET scans. This work presents a fully-automated segmentation technique using a…
Assessing Privacy Leakage in Synthetic 3-D PET Imaging using Transversal GAN
Robert V. Bergen, Jean-Francois Rajotte, Fereshteh Yousefirizi +2
Training computer-vision related algorithms on medical images for disease diagnosis or image segmentation is difficult in large part due to privacy concerns. For this reason, gener…
Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Features
Arman Rahmim, Amirhosein Toosi, Mohammad R. Salmanpour +9
Radiomics features extract quantitative information from medical images, towards the derivation of biomarkers for clinical tasks, such as diagnosis, prognosis, or treatment respons…
Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks
Fereshteh Yousefirizi, Ian Janzen, Natalia Dubljevic +4
We utilized a 3D nnU-Net model with residual layers supplemented by squeeze and excitation (SE) normalization for tumor segmentation from PET/CT images provided by the Head and Nec…
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…
AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics
Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar +3
Artificial intelligence (AI) techniques have significant potential to enable effective, robust and automated image phenotyping including identification of subtle patterns. AI-based…
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…