Area of research
Radiology, Nuclear Medicine and Imaging · Genetics
Research interest
Research focused on Artificial intelligence and Radiomics, with related work in Neuroradiology, Coronavirus disease 2019 (COVID-19), Segmentation. Notable publications include 'Rapid identification of COVID-19 severity in CT scans through classification of deep features', 'BTSC-TNAS: A neural architecture search-based transformer for brain tumor segmentation and classification', and 'BTMF-GAN: A multi-modal MRI fusion generative adversarial network for brain tumors'.
Understanding the Causes of Delayed Decision-Making by Family Members of Stroke Patients Eligible for Thrombolytic Therapy
Harnessing routine MRI for the early screening of Parkinson’s disease: a multicenter machine learning study using T2-weighted FLAIR imaging
Altered whole-brain functional network in patients with frontal low-grade gliomas: a resting-state functional MRI study
BTSC-TNAS: A neural architecture search-based transformer for brain tumor segmentation and classification
BTMF-GAN: A multi-modal MRI fusion generative adversarial network for brain tumors
Development and validation of a radiomics-based prediction pipeline for the response to stereotactic radiosurgery therapy in brain metastases
Development and validation of a machine learning algorithm for predicting diffuse midline glioma, H3 K27–altered, H3 K27 wild-type high-grade glioma, and primary CNS lymphoma of the brain midline in adults
Rapid identification of COVID-19 severity in CT scans through classification of deep features