Justin Szeto

According to our database, Justin Szeto authored at least 14 papers between 2021 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge.

Asbjørn Munk, Stefano Cerri, Vardan Nersesjan, Christian Hedeager Krag, Jakob Ambsdorf, Pablo Rocamora-García, Julia Machnio, Peirong Liu, Suhyun Ahn, Nasrin Akbari, Yasmina Al Khalil, Kimberly Amador, Sina Amirrajab, Tal Arbel, Meritxell Bach Cuadra, Ujjwal Baid, Bhakti Baheti, Jaume Banus, Kamil Barbierik, Christoph Brune, Yansong Bu, Baptiste Callard, Yuhan Chen, Cornelius Crijnen, Corentin Dancette, Peter Drotár, Prasad Dutande, Nils D. Forkert, Saurabh Garg, Jakub Gazda, Matej Gazda, Benoît Gérin, Partha Ghosh, Weikang Gong, Pedro M. Gordaliza, Sam Hashemi, Tobias Heimann, Fucang Jia, Jiexin Jiang, Emily Kaczmarek, Chris Kang, Seung Kwan Kang, Mohammad Khazaei, Julien Khlaut, Petros Koutsouvelis, Jae Sung Lee, Yuchong Li, Mengye Lyu, Mingchen Ma, Anant Madabhushi, Klaus H. Maier-Hein, Pierre Manceron, Andrés Martínez Mora, Moona Mazher, Felix Meister, Nataliia Molchanova, Steven A. Niederer, Leonard Nürnberg, Jinah Park, Abdul Qayyum, Jonas Richiardi, Antoine Saporta, Branislav Setlák, Ning Shen, Justin Szeto, Constantin Ulrich, Puru Vaish, Vibujithan Vigneshwaran, Leroy Volmer, Zihao Wang, Siqi Wei, Anthony J. Winder, Jelmer M. Wolterink, Maxence Wynen, Chang Yang, Si Young Yie, Mostafa Mehdipour-Ghazi, Akshay Pai, Espen Jimenez-Solem, Sebastian Nørgaard Llambias, Mikael Boesen, Michael Eriksen Benros, Juan Eugenio Iglesias, Mads Nielsen

CoRR, April, 2026

2025

KerJEPA: Kernel Discrepancies for Euclidean Self-Supervised Learning.

Eric Zimmermann, Harley Wiltzer, Justin Szeto, David Alvarez-Melis, Lester Mackey

CoRR, December, 2025

Exposing and Mitigating Calibration Biases and Demographic Unfairness in MLLM Few-Shot In-Context Learning for Medical Image Classification.

Xing Shen, Justin Szeto, Mingyang Li, Hengguan Huang, Tal Arbel

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2025, 2025

SSL-AD: Spatiotemporal Self-supervised Learning for Generalizability and Adaptability Across Alzheimer's Prediction Tasks and Datasets.

Emily Kaczmarek, Justin Szeto, Brennan Nichyporuk, Tal Arbel

Proceedings of the Learning with Longitudinal Medical Images and Data, 2025

Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments.

Gian Mario Favero, Ge Ya Luo, Nima Fathi, Justin Szeto, Douglas L. Arnold, Brennan Nichyporuk, Chris Pal, Tal Arbel

Proceedings of the Learning with Longitudinal Medical Images and Data, 2025

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses.

Emily Kaczmarek, Justin Szeto, Brennan Nichyporuk, Tal Arbel

Proceedings of the IEEE/CVF International Conference on Computer Vision, ICCV 2025, 2025

2023

Benchmarking a Benchmark: How Reliable is MS-COCO?

Eric Zimmermann, Justin Szeto, Jérôme Pasquero, Frédéric Ratle

CoRR, 2023

An Empirical Study of Uncertainty in Polygon Annotation and the Impact of Quality Assurance.

Eric Zimmermann, Justin Szeto, Frédéric Ratle

CoRR, 2023

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

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

2022

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

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

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, Dmitriy Serdyuk, Tal Arbel, Chris Pal, Gaël Varoquaux, Pascal Vincent

CoRR, 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, Tal Arbel, Chris Pal, Gaël Varoquaux, Pascal Vincent

Proceedings of the Fourth Conference on Machine Learning and Systems, 2021

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

Proceedings of the Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health, 2021