activity
20172020
most citedAssociation of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

340 citations · 357 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG20202 cited

Machine learning applications using diffusion tensor imaging of human brain: A PubMed literature review

Ashirbani Saha, Pantea Fadaiefard, Jessica E. Rabski +2

We performed a PubMed search to find 148 papers published between January 2010 and December 2019 related to human brain, Diffusion Tensor Imaging (DTI), and Machine Learning (ML).…

eess.IV2019340 cited

Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

Mateusz Buda, Ashirbani Saha, Maciej A Mazurowski

Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quant…

cs.CV2018

Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images

Jun Zhang, Ashirbani Saha, Brian J. Soher +1

Objective: To develop an automatic image normalization algorithm for intensity correction of images from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acqui…

cs.CV2018

Deep learning in radiology: an overview of the concepts and a survey of the state of the art

Maciej A. Mazurowski, Mateusz Buda, Ashirbani Saha +1

Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep l…

cs.CV201712 cited

Deep Learning for identifying radiogenomic associations in breast cancer

Zhe Zhu, Ehab Albadawy, Ashirbani Saha +3

Purpose: To determine whether deep learning models can distinguish between breast cancer molecular subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).…

cs.CV20173 cited

Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ

Zhe Zhu, Michael Harowicz, Jun Zhang +4

Purpose: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the di- agnosis of ductal ca…