In recent years there has been an increased thrust to understand and quantify the complex information conveyed by medical images. Developing modern computational techniques that offer the potential for extracting diverse and complex information from imaging data and applying these to a plethora of clinical studies is crucial. These techniques not only support precise quantification but also overcome the limitations of subjective visual interpretation. Furthermore, these methods can facilitate finding specific markers that relate to pathologies as well as aid in treatment planning.
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"Open-Set Recognition for Skin Lesions Using Dermoscopic Images"
International Workshop on Machine Learning in Medical Imaging, 614-623
"Normative Baseline for Radiomics in Brain MRI: Evaluating the Robustness, Regional Variations, and Reproducibility on FLAIR Images"
Journal of Magnetic Resonance Imaging
"In Vivo Evaluation of White Matter Abnormalities in Children with Duchenne Muscular Dystrophy Using DTI"
American Journal of Neuroradiology 41 (7), 1271-1278
"Abnormalities in the white matter tracts in patients with Parkinson disease and psychosis”
Neurology 94 (18), e1876-e1884
