Unsupervised Machine Learning in Pathology
| Autor: | Ugljesa Djuric, Phedias Diamandis, Adil Roohi, Kevin Faust |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Pathology medicine.medical_specialty Human intelligence business.industry Deep learning Pathology and Forensic Medicine 03 medical and health sciences Computational pathology 030104 developmental biology 0302 clinical medicine Workflow 030220 oncology & carcinogenesis Medicine Unsupervised learning Surgery Artificial intelligence Applications of artificial intelligence business |
| Zdroj: | Surgical Pathology Clinics. 13:349-358 |
| ISSN: | 1875-9181 |
| Popis: | Applications of artificial intelligence and particularly deep learning to aid pathologists in carrying out laborious and qualitative tasks in histopathologic image analysis have now become ubiquitous. We introduce and illustrate how unsupervised machine learning workflows can be deployed in existing pathology workflows to begin learning autonomously through exploration and without the need for extensive direction. Although still in its infancy, this type of machine learning, which more closely mirrors human intelligence, stands to add another exciting layer of innovation to computational pathology and accelerate the transition to autonomous pathologic tissue analysis. |
| Databáze: | OpenAIRE |
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