Okyaz Eminaga, MD/PhD

Presented research on using AI-driven digital biomarkers for prostate cancer risk stratification, emphasizing the need for clear boundaries and calibrated prediction models.

Co-authored a publication on using AI to interpret malignancy grades in prostate cancer from histology images.

Co-authored research evaluating the role of AI as a digital twin for pathologists in prostate cancer diagnosis.

Contributed to developing an efficient augmented intelligence framework for detecting bladder lesions.

Co-authored a paper introducing PlexusNet, a neural network concept for medical image classification.

Published research on a framework for efficient video documentation of bladder lesions during cystoscopy.

Authored research on the spread patterns of prostate cancer, published in World Journal of Urology.

Co-authored a paper on using deep convolutional neural networks for diagnostic classification of cystoscopic images.

Published research on a CMDX-based information system for quality management and clinical research in prostate cancer.

Authored a paper on a clinical map document based on XML for reporting and analyzing prostate cancer specimens.