20 citations
- McMaster UniversityCA4 papers
- ImpactCA2 papers
- Mayo Clinic in ArizonaUS2 papers
- Mayo Clinic in FloridaUS2 papers
- Population Health Research InstituteCA2 papers
- William Osler Health SystemCA2 papers
- Aston UniversityGB1 paper
- Brockhouse Institute for Materials ResearchCA1 paper
- Canada–France–Hawaii TelescopeUS1 paper
- Institut d'Astrophysique de ParisFR1 paper
- Lagrange LaboratoryFR1 paper
- Massachusetts Institute of TechnologyUS1 paper
Showing 2023Show all
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cs.CV2023★ 17 cited
Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review
Ricardo Gonzalez, Peyman Nejat, Ashirbani Saha +3
Numerous machine learning (ML) models have been developed for breast cancer using various types of data. Successful external validation (EV) of ML models is important evidence of t…
cs.CV2023★ 20 cited
Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities
Ricardo Gonzalez, Ashirbani Saha, Clinton J. V. Campbell +3
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Syst…