693 citations · 2k across the 7 of their papers we have counts for
7 papers
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
Segmentation and genome annotation algorithms
Maxwell W Libbrecht, Rachel CW Chan, Michael M Hoffman
Segmentation and genome annotation (SAGA) algorithms are widely used to understand genome activity and gene regulation. These algorithms take as input epigenomic datasets, such as…
The MCC-F1 curve: a performance evaluation technique for binary classification
Chang Cao, Davide Chicco, Michael M. Hoffman
Many fields use the ROC curve and the PR curve as standard evaluations of binary classification methods. Analysis of ROC and PR, however, often gives misleading and inflated perfor…
The importance of transparency and reproducibility in artificial intelligence research
Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny +17
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines…
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities
Marinka Zitnik, Francis Nguyen, Bo Wang +3
New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include a myriad of properties de…