Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation. - Brennan Nichyporuk; Jillian Cardinell; Justin Szeto; Raghav Mehta; Sotirios A. Tsaftaris; Douglas L. Arnold; Tal Arbel (DART/FAIR@MICCAI, 2021)

Accounting for Variance in Machine Learning Benchmarks. - Xavier Bouthillier; Pierre Delaunay; Mirko Bronzi; Assya Trofimov; Brennan Nichyporuk; Justin Szeto; Nazanin Mohammadi Sepahvand; Edward Raff; Kanika Madan; Vikram Voleti; Samira Ebrahimi Kahou; Vincent Michalski; et al. (MLSys, 2021)

Accounting for Variance in Machine Learning Benchmarks. - Xavier Bouthillier; Pierre Delaunay; Mirko Bronzi; Assya Trofimov; Brennan Nichyporuk; Justin Szeto; Naz Sepah; Edward Raff; Kanika Madan; Vikram Voleti; Samira Ebrahimi Kahou; Vincent Michalski; et al. (CoRR, 2021)

Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting. - Brennan Nichyporuk; Justin Szeto; Douglas L. Arnold; Tal Arbel (CoRR, 2021)

Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation. - Brennan Nichyporuk; Jillian Cardinell; Justin Szeto; Raghav Mehta; Jean-Pierre R. Falet; Douglas L. Arnold; Sotirios A. Tsaftaris; Tal Arbel (CoRR, 2022)

Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis. - Changjian Shui; Justin Szeto; Raghav Mehta; Douglas L. Arnold; Tal Arbel (MICCAI (3), 2023)

An Empirical Study of Uncertainty in Polygon Annotation and the Impact of Quality Assurance. - Eric Zimmermann; Justin Szeto; Frédéric Ratle (CoRR, 2023)

Benchmarking a Benchmark: How Reliable is MS-COCO? - Eric Zimmermann; Justin Szeto; Jérôme Pasquero; Frédéric Ratle (CoRR, 2023)