1 citations · 1 across the 2 of their papers we have counts for
5 papers
CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis
Indrani Bhattacharya, Arun Seetharaman, Wei Shao +10
Magnetic Resonance Imaging (MRI) is widely used for screening and staging prostate cancer. However, many prostate cancers have subtle features which are not easily identifiable on…
Deep Learning for Prostate Pathology
Okyaz Eminaga, Yuri Tolkach, Christian Kunder +13
The current study detects different morphologies related to prostate pathology using deep learning models; these models were evaluated on 2,121 hematoxylin and eosin (H&E) stain hi…
Biologic and Prognostic Feature Scores from Whole-Slide Histology Images Using Deep Learning
Okyaz Eminaga, Mahmood Abbas, Yuri Tolkach +4
Histopathology is a reflection of the molecular changes and provides prognostic phenotypes representing the disease progression. In this study, we introduced feature scores generat…
Plexus Convolutional Neural Network (PlexusNet): A novel neural network architecture for histologic image analysis
Okyaz Eminaga, Mahmoud Abbas, Christian Kunder +5
Different convolutional neural network (CNN) models have been tested for their application in histological image analyses. However, these models are prone to overfitting due to the…
Registration of pre-surgical MRI and whole-mount histopathology images in prostate cancer patients with radical prostatectomy via RAPSODI
Mirabela Rusu, Christian A. Kunder, Nikola C. Teslovich +9
Magnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis. It can spare men with a normal exam from undergoing invasive biopsy while making biopsies…