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Saima Rathore

Showing results (31-40 of 46) with videos related to

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Scientific Reports|March 25, 2018
Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1Saima Rathore, Hamed Akbari, Martin Rozycki, et al.
JAMA Pediatrics|December 20, 2017
Use of Fetal Magnetic Resonance Image Analysis and Machine Learning to Predict the Need for Postnatal Cerebrospinal Fluid Diversion in Fetal VentriculomegalyJared M Pisapia, Hamed Akbari, Martin Rozycki, et al.
Journal of Neuroradiology = Journal De Neuroradiologie|August 31, 2023
AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methodsGirish Bathla, Durjoy Deb Dhruba, Neetu Soni, et al.
Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association|January 28, 2026
Biomarkers in patients with clinical signs of mild cognitive impairment or mild Alzheimer's disease but without amyloid deposits on positron emission tomography: Results from Bio-Hermes Study participantsRichard C Mohs, Douglas W Beauregard, Lynne Hughes, et al.
Cancer|March 5, 2020
Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastomaHamed Akbari, Saima Rathore, Spyridon Bakas, et al.
Neuro-Oncology Advances|February 1, 2021
Multi-institutional noninvasive in vivo characterization of <i>IDH</i>, 1p/19q, and EGFRvIII in glioma using neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk)Saima Rathore, Suyash Mohan, Spyridon Bakas, et al.
Neuroimage|July 1, 2020
Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic trainingSiddhesh Thakur, Jimit Doshi, Sarthak Pati, et al.
Brainlesion : Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Brainles (Workshop)|August 6, 2020
The Cancer Imaging Phenomics Toolkit (CaPTk): Technical OverviewSarthak Pati, Ashish Singh, Saima Rathore, et al.
Journal of Medical Imaging (Bellingham, Wash.)|January 18, 2018
Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcomeChristos Davatzikos, Saima Rathore, Spyridon Bakas, et al.
Applied Sciences (Basel, Switzerland)|October 8, 2021
Interactive Machine Learning-Based Multi-Label Segmentation of Solid Tumors and OrgansDimitrios Bounias, Ashish Singh, Spyridon Bakas, et al.
Pageof 5

Showing results (31-40 of 46) with videos related to

Sort By:
Pageof 5
Scientific Reports|March 25, 2018
Radiomic MRI signature reveals three distinct subtypes of glioblastoma with different clinical and molecular characteristics, offering prognostic value beyond IDH1Saima Rathore, Hamed Akbari, Martin Rozycki, et al.
JAMA Pediatrics|December 20, 2017
Use of Fetal Magnetic Resonance Image Analysis and Machine Learning to Predict the Need for Postnatal Cerebrospinal Fluid Diversion in Fetal VentriculomegalyJared M Pisapia, Hamed Akbari, Martin Rozycki, et al.
Journal of Neuroradiology = Journal De Neuroradiologie|August 31, 2023
AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methodsGirish Bathla, Durjoy Deb Dhruba, Neetu Soni, et al.
Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association|January 28, 2026
Biomarkers in patients with clinical signs of mild cognitive impairment or mild Alzheimer's disease but without amyloid deposits on positron emission tomography: Results from Bio-Hermes Study participantsRichard C Mohs, Douglas W Beauregard, Lynne Hughes, et al.
Cancer|March 5, 2020
Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastomaHamed Akbari, Saima Rathore, Spyridon Bakas, et al.
Neuro-Oncology Advances|February 1, 2021
Multi-institutional noninvasive in vivo characterization of <i>IDH</i>, 1p/19q, and EGFRvIII in glioma using neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk)Saima Rathore, Suyash Mohan, Spyridon Bakas, et al.
Neuroimage|July 1, 2020
Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic trainingSiddhesh Thakur, Jimit Doshi, Sarthak Pati, et al.
Brainlesion : Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Brainles (Workshop)|August 6, 2020
The Cancer Imaging Phenomics Toolkit (CaPTk): Technical OverviewSarthak Pati, Ashish Singh, Saima Rathore, et al.
Journal of Medical Imaging (Bellingham, Wash.)|January 18, 2018
Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcomeChristos Davatzikos, Saima Rathore, Spyridon Bakas, et al.
Applied Sciences (Basel, Switzerland)|October 8, 2021
Interactive Machine Learning-Based Multi-Label Segmentation of Solid Tumors and OrgansDimitrios Bounias, Ashish Singh, Spyridon Bakas, et al.
Pageof 5