Hamed Akbari
scholar.google.com
0000-0001-9786-3707
University of Pennsylvania
40 papers found
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The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics
Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine
Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics
SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography
Reproducibility analysis of multi‐institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset
Histopathology‐validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo‐progression in glioblastoma
Patient-Specific Registration of Pre-operative and Post-recurrence Brain Tumor MRI Scans
Epidermal Growth Factor Receptor Extracellular Domain Mutations in Glioblastoma Present Opportunities for Clinical Imaging and Therapeutic Development
In vivoevaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature
Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning
Use of Fetal Magnetic Resonance Image Analysis and Machine Learning to Predict the Need for Postnatal Cerebrospinal Fluid Diversion in Fetal Ventriculomegaly
Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome
Brain Cancer Imaging Phenomics Toolkit (brain-CaPTk): An Interactive Platform for Quantitative Analysis of Glioblastoma
Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
Correlations of atrial diameter and frontooccipital horn ratio with ventricle size in fetal ventriculomegaly
Population-based MRI atlases of spatial distribution are specific to patient and tumor characteristics in glioblastoma
Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative Framework
Brain Lesions, Introduction
Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma:
Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques
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