9 citations · 12 across the 6 of their papers we have counts for
6 papers
Optimizing Rank for High-Fidelity Implicit Neural Representations
Julian McGinnis, Florian A. Hölzl, Suprosanna Shit +6
Implicit Neural Representations (INRs) based on vanilla Multi-Layer Perceptrons (MLPs) are widely believed to be incapable of representing high-frequency content. This has directed…
Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images
Chinmay Prabhakar, Hongwei Bran Li, Johannes C. Paetzold +6
Multiple Sclerosis (MS) is a severe neurological disease characterized by inflammatory lesions in the central nervous system. Hence, predicting inflammatory disease activity is cru…
A Lightweight Generative Model for Interpretable Subject-level Prediction
Chiara Mauri, Stefano Cerri, Oula Puonti +2
Recent years have seen a growing interest in methods for predicting an unknown variable of interest, such as a subject's diagnosis, from medical images depicting its anatomical-fun…
Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations
Julian McGinnis, Suprosanna Shit, Hongwei Bran Li +9
Clinical routine and retrospective cohorts commonly include multi-parametric Magnetic Resonance Imaging; however, they are mostly acquired in different anisotropic 2D views due to…
An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation
Stefano Cerri, Douglas N. Greve, Andrew Hoopes +4
In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that…
A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
Stefano Cerri, Andrew Hoopes, Douglas N. Greve +2
In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis. The method builds upon an existing cross…