1 citations · 2 across the 3 of their papers we have counts for
5 papers
Critical Evaluation of Artificial Intelligence as Digital Twin of Pathologist for Prostate Cancer Pathology
Okyaz Eminaga, Mahmoud Abbas, Christian Kunder +10
Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading pat…
Conceptual Framework and Documentation Standards of Cystoscopic Media Content for Artificial Intelligence
Okyaz Eminaga, Timothy Jiyong Lee, Jessie Ge +5
Background: The clinical documentation of cystoscopy includes visual and textual materials. However, the secondary use of visual cystoscopic data for educational and research purpo…
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…