Abstract
Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.
Original language | English (US) |
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Article number | 100177 |
Journal | Journal of Pathology Informatics |
Volume | 14 |
DOIs | |
State | Published - Jan 2023 |
Keywords
- Artificial intelligence
- Computational pathology
- Digital pathology
- Genitourinary pathology
ASJC Scopus subject areas
- Pathology and Forensic Medicine
- Health Informatics
- Computer Science Applications