3 citations · 10 across the 8 of their papers we have counts for
8 papers
Bridging the gap between prostate radiology and pathology through machine learning
Indrani Bhattacharya, David S. Lim, Han Lin Aung +11
Prostate cancer is the second deadliest cancer for American men. While Magnetic Resonance Imaging (MRI) is increasingly used to guide targeted biopsies for prostate cancer diagnosi…
Weakly Supervised Registration of Prostate MRI and Histopathology Images
Wei Shao, Indrani Bhattacharya, Simon J. C. Soerensen +7
The interpretation of prostate MRI suffers from low agreement across radiologists due to the subtle differences between cancer and normal tissue. Image registration addresses this…
ProsRegNet: A Deep Learning Framework for Registration of MRI and Histopathology Images of the Prostate
Wei Shao, Linda Banh, Christian A. Kunder +10
Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observ…
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…