most citedSeeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities

20 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

3 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…

stat.AP2019★ 5 cited

Present criteria for prophylactic ICD implantation: Insights from the EU-CERT-ICD (Comparative Effectiveness Research to Assess the Use of Primary ProphylacTic Implantable Cardioverter Defibrillators in EUrope) project

M. Zabel, S. Schlögl, A. Lubinski +45

BACKGROUND. The clinical effectiveness of primary prevention implantable cardioverter defibrillator (ICD) therapy is under debate. It is urgently needed to better identify patients…