Search Results for author:
Found 5 papers, 1 papers with code
Conceptual Framework and Documentation Standards of Cystoscopic Media Content for Artificial Intelligence
Background: The clinical documentation of cystoscopy includes visual and textual materials.
Plexus Convolutional Neural Network (PlexusNet): A novel neural network architecture for histologic image analysis
A well-fitted PlexusNet-based model delivered comparable classification performance (AUC: 0. 963) in distinguishing prostate cancer from healthy tissues, although it was at least 23 times smaller, had a better model calibration and clinical utility than the comparison models.
Deep Learning for Prostate Pathology
For case usage, these models were applied for the annotation tasks in clinician-oriented pathology reports for prostatectomy specimens.
Biologic and Prognostic Feature Scores from Whole-Slide Histology Images Using Deep Learning
Histopathology is a reflection of the molecular changes and provides prognostic phenotypes representing the disease progression.
Critical Evaluation of Artificial Intelligence as Digital Twin of Pathologist for Prostate Cancer Pathology
Adjusting the decision threshold for the secondary Gleason pattern from 5% to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (kappa from 0. 44 to 0. 64).
