40 citations · 42 across the 9 of their papers we have counts for
5 papers · 1 filter
Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments
Gian Mario Favero, Ge Ya Luo, Nima Fathi +5
Image-based personalized medicine has the potential to transform healthcare, particularly for diseases that exhibit heterogeneous progression such as Multiple Sclerosis (MS). In th…
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 +2
Multimodal large language models (MLLMs) have enormous potential to perform few-shot in-context learning in the context of medical image analysis. However, safe deployment of these…
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…
Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation
Brennan Nichyporuk, Jillian Cardinell, Justin Szeto +4
Many automatic machine learning models developed for focal pathology (e.g. lesions, tumours) detection and segmentation perform well, but do not generalize as well to new patient c…
Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting
Brennan Nichyporuk, Justin Szeto, Douglas L. Arnold +1
There are many clinical contexts which require accurate detection and segmentation of all focal pathologies (e.g. lesions, tumours) in patient images. In cases where there are a mi…