An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS) Review
| Authors: | Parwani, A. V.; Patel, A.; Zhou, M.; Cheville, J. C.; Tizhoosh, H.; Humphrey, P.; Reuter, V. E.; True, L. D. |
| Review Title: | An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS) |
| 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. © 2023 |
| Keywords: | artificial intelligence; digital pathology; computational pathology; genitourinary pathology |
| Journal Title: | Journal of Pathology Informatics |
| Volume: | 14 |
| ISSN: | 2229-5089 |
| Publisher: | Wolters Kluwer - Medknow |
| Date Published: | 2023-01-01 |
| Start Page: | 100177 |
| Language: | English |
| DOI: | 10.1016/j.jpi.2022.100177 |
| PROVIDER: | scopus |
| PMCID: | PMC9841212 |
| PUBMED: | 36654741 |
| DOI/URL: | |
| Notes: | Review -- Export Date: 1 February 2023 -- Source: Scopus |
