103 citations · 105 across the 7 of their papers we have counts for
7 papers
Mitigating Calibration Bias Without Fixed Attribute Grouping for Improved Fairness in Medical Imaging Analysis
Changjian Shui, Justin Szeto, Raghav Mehta +2
Trustworthy deployment of deep learning medical imaging models into real-world clinical practice requires that they be calibrated. However, models that are well calibrated overall…
Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models
Joshua Durso-Finley, Jean-Pierre Falet, Raghav Mehta +3
Image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve their clinical outcome. Machine learning fr…
Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors
Anjun Hu, Jean-Pierre R. Falet, Brennan S. Nichyporuk +4
We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain lesions or atrophy) from subject-specific…
Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation
Brennan Nichyporuk, Jillian Cardinell, Justin Szeto +5
Generalization is an important attribute of machine learning models, particularly for those that are to be deployed in a medical context, where unreliable predictions can have real…
Heatmap Regression for Lesion Detection using Pointwise Annotations
Chelsea Myers-Colet, Julien Schroeter, Douglas L. Arnold +1
In many clinical contexts, detecting all lesions is imperative for evaluating disease activity. Standard approaches pose lesion detection as a segmentation problem despite the time…
Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers
Amar Kumar, Anjun Hu, Brennan Nichyporuk +4
The discovery of patient-specific imaging markers that are predictive of future disease outcomes can help us better understand individual-level heterogeneity of disease evolution.…
Statistical power and prediction accuracy in multisite resting-state fMRI connectivity
Christian Dansereau, Yassine Benhajali, Celine Risterucci +4
Connectivity studies using resting-state functional magnetic resonance imaging are increasingly pooling data acquired at multiple sites. While this may allow investigators to speed…