Uncertainty estimation with a finite dataset in the assessment of classification models. - Weijie Chen 007; Waleed A. Yousef; Brandon D. Gallas; Elizabeth R. Hsu; Samir Lababidi; Rong Tang; Gene A. Pennello; W. Fraser Symmans; Lajos Pusztai (Comput. Stat. Data Anal., 2012)

Deciphering deep ensembles for lung nodule analysis. - Ravi K. Samala; Berkman Sahiner; Gene Pennello; Kenny H. Cha; Mohammad Mehdi Farhangi; Nicholas Petrick (Computer-Aided Diagnosis, 2022)

Sequential algorithmic modification with test data reuse. - Jean Feng; Gene Pennello; Nicholas Petrick; Berkman Sahiner; Romain Pirracchio; Alexej Gossmann (UAI, 2022)

Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions. - Jean Feng; Alexej Gossmann; Gene Pennello; Nicholas Petrick; Berkman Sahiner; Romain Pirracchio (CoRR, 2022)

Decision region analysis to deconstruct the subgroup influence on AI/ML predictions. - Alexis Burgon; Nicholas Petrick; Berkman Sahiner; Gene Pennello; Ravi K. Samala (Computer-Aided Diagnosis, 2023)

Towards a Post-Market Monitoring Framework for Machine Learning-based Medical Devices: A case study. - Jean Feng; Adarsh Subbaswamy; Alexej Gossmann; Harvineet Singh; Berkman Sahiner; Mi-Ok Kim; Gene Pennello; Nicholas Petrick; Romain Pirracchio; Fan Xia (CoRR, 2023)

Is this model reliable for everyone? Testing for strong calibration. - Jean Feng; Alexej Gossmann; Romain Pirracchio; Nicholas Petrick; Gene Pennello; Berkman Sahiner (AISTATS, 2024)

Monitoring machine learning-based risk prediction algorithms in the presence of performativity. - Jean Feng; Alexej Gossmann; Gene Pennello; Nicholas Petrick; Berkman Sahiner; Romain Pirracchio (AISTATS, 2024)

Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens. - Jean Feng; Adarsh Subbaswamy; Alexej Gossmann; Harvineet Singh; Berkman Sahiner; Mi-Ok Kim; Gene Anthony Pennello; Nicholas Petrick; Romain Pirracchio; Fan Xia (CLeaR, 2024)