activity
20132023
most citedPyXNAT: XNAT in Python

37 citations · 112 across the 6 of their papers we have counts for

collaborators

7 papers

q-bio.QM2023★ 3 cited

Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Zhijian Yang, Junhao Wen, Ahmed Abdulkadir +25

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multipl…

eess.IV2022★ 36 cited

MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network

Satrajit Chakrabarty, Pamela LaMontagne, Joshua Shimony +2

Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status are important prognostic markers for glioma. Currently, they are determined using invasive procedures. Our goal…

eess.IV2022★ 9 cited

Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)

Satrajit Chakrabarty, Syed Amaan Abidi, Mina Mousa +12

Efforts to utilize growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling owing to the data heterogeneity. Her…

eess.IV2021★ 27 cited

The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

Bhakti Baheti, Satrajit Chakrabarty, Hamed Akbari +70

Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been progress in developing gene…

eess.IV2021

Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy

Rajarajeswari Muthusivarajan, Adrian Celaya, Joshua P. Yung +4

Deep neural networks with multilevel connections process input data in complex ways to learn the information.A networks learning efficiency depends not only on the complex neural n…

cs.CV2018

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, Mauricio Reyes, Andras Jakab +421

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritum…