20 citations · 37 across the 2 of their papers we have counts for
2 papers
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…