3 citations · 6 across the 4 of their papers we have counts for
5 papers
Segmentation-Consistent Probabilistic Lesion Counting
Julien Schroeter, Chelsea Myers-Colet, Douglas L Arnold +1
Lesion counts are important indicators of disease severity, patient prognosis, and treatment efficacy, yet counting as a task in medical imaging is often overlooked in favor of seg…
Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI
Joshua Durso-Finley, Jean-Pierre R. Falet, Brennan Nichyporuk +2
Precision medicine for chronic diseases such as multiple sclerosis (MS) involves choosing a treatment which best balances efficacy and side effects/preferences for individual patie…
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…
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation
Tanya Nair, Doina Precup, Douglas L. Arnold +1
Deep learning (DL) networks have recently been shown to outperform other segmentation methods on various public, medical-image challenge datasets [3,11,16], especially for large pa…