Prof. Habib Zaidi

Division of Nuclear Medicine and Molecular Imaging

Geneva University Hospital, Switzerland

Professor

Email: [email protected]

Qualifications

2000 Ph.D., Geneva University, Medical Physics

Publications

1. Montes A L, Yousefirizi F, Chen Y, et al. Artificial intelligence for simplified patient-centered dosimetry in radiopharmaceutical therapies[J]. PET clinics, 2026, 21(1): 73-88.
2. Hosseini S A, Hajianfar G, Hall B, et al. Robust vs. Non-robust radiomic features: the quest for optimal machine learning models using phantom and clinical studies[J]. Cancer Imaging, 2025, 25(1): 1-15.
3. Bagheri S, Hajianfar G, Sabouri M, et al. Impact of Field-of-view Zooming and Segmentation Batches on Radiomics Features Reproducibility and Machine Learning Performance in Thyroid Scintigraphy[J]. Clinical Nuclear Medicine, 2025, 50(8): 683-694.
4. Strigari L, Schwarz J, Bradshaw T, et al. Computational nuclear oncology toward precision radiopharmaceutical therapies: ethical, regulatory, and socioeconomic dimensions of theranostic digital twins[J]. Journal of Nuclear Medicine, 2025, 66(5): 748-756.
5. Whybra P, Zwanenburg A, Andrearczyk V, et al. The image biomarker standardization initiative: standardized convolutional filters for reproducible radiomics and enhanced clinical insights[J]. Radiology, 2024, 310(2): e231319.
6. Abdollahi H, Yousefirizi F, Shiri I, et al. Theranostic digital twins: Concept, framework and roadmap towards personalized radiopharmaceutical therapies[J]. Theranostics, 2024, 14(9): 3404.
7. Hajianfar G, Haddadi Avval A, Hosseini S A, et al. Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics[J]. La radiologia medica, 2023, 128(12): 1521-1534.
8. Shiri I, Salimi Y, Maghsudi M, et al. Differential privacy preserved federated transfer learning for multi-institutional 68Ga-PET image artefact detection and disentanglement[J]. European journal of nuclear medicine and molecular imaging, 2023, 51(1): 40-53.
9. Shiri I, Razeghi B, Sadr A V, et al. Multi-institutional PET/CT image segmentation using federated deep transformer learning[J]. Computer Methods and Programs in Biomedicine, 2023, 240: 107706.
10. Shiri I, Vafaei Sadr A, Akhavan A, et al. Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning[J]. European Journal of Nuclear Medicine and Molecular Imaging, 2023, 50(4): 1034-1050.
11. Avard E, Shiri I, Hajianfar G, et al. Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection[J]. Computers in Biology and Medicine, 2022, 141: 105145.
12. Andrearczyk V, Oreiller V, Abobakr M, et al. Overview of the HECKTOR challenge at MICCAI 2022: automatic head and neck tumor segmentation and outcome prediction in PET/CT[M]//3D Head and Neck Tumor Segmentation in PET/CT Challenge. Cham: Springer Nature Switzerland, 2022: 1-30.
13. Edalat-Javid M, Shiri I, Hajianfar G, et al. Cardiac SPECT radiomic features repeatability and reproducibility: A multi-scanner phantom study[J]. Journal of Nuclear Cardiology, 2021, 28(6): 2730-2744.
14. Shiri I, Sabet K A M, Arabi H, et al. Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks[J]. Journal of Nuclear Cardiology, 2021, 28(6): 2761-2779.
15. Andrearczyk V, Oreiller V, Boughdad S, et al. Overview of the HECKTOR challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET/CT images[M]//3D head and neck tumor segmentation in PET/CT challenge. Cham: Springer International Publishing, 2021: 1-37.
16. Khodabakhshi Z, Mostafaei S, Arabi H, et al. Non-small cell lung carcinoma histopathological subtype phenotyping using high-dimensional multinomial multiclass CT radiomics signature[J]. Computers in biology and medicine, 2021, 136: 104752.
17. Sanaat A, Shiri I, Arabi H, et al. Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging[J]. European journal of nuclear medicine and molecular imaging, 2021, 48(8): 2405-2415.
18. Arabi H, AkhavanAllaf A, Sanaat A, et al. The promise of artificial intelligence and deep learning in PET and SPECT imaging[J]. Physica Medica, 2021, 83: 122-137.
19. Bahrami A, Karimian A, Fatemizadeh E, et al. A new deep convolutional neural network design with efficient learning capability: Application to CT image synthesis from MRI[J]. Medical physics, 2020, 47(10): 5158-5171.
20. Arabi H, Zaidi H. Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy[J]. European Journal of Hybrid Imaging, 2020, 4(1): 17.
21. Arabi H, Zeng G, Zheng G, et al. Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI[J]. European journal of nuclear medicine and molecular imaging, 2019, 46(13): 2746-2759.
22. Fahrni G, Karakatsanis N A, Di Domenicantonio G, et al. Does whole-body Patlak 18F-FDG PET imaging improve lesion detectability in clinical oncology?[J]. European radiology, 2019, 29(9): 4812-4821.
23. Rahmim A, Lodge M A, Karakatsanis N A, et al. Dynamic whole-body PET imaging: principles, potentials and applications[J]. European journal of nuclear medicine and molecular imaging, 2019, 46(2): 501-518.

Profile Details

https://neurocenter-unige.ch/research-groups/habib-zaidi/

https://www.researchgate.net/profile/Habib-Zaidi

https://scholar.google.com/citations?user=uJkubCgAAAAJ&hl=fr   

WoS ResearcherID: I-4669-2017