179 citations · 225 across the 4 of their papers we have counts for
5 papers
NeuroCBIR: A Fast and Accurate Image Retrieval System for Whole-Brain and Region-Specific MRI
Felix Nieto-del-Amor, Jingru Fu, J. -Sebastian Muehlboeck +3
Content-based image retrieval (CBIR) in neuroimaging enables the identification of structurally similar brain scans, supporting diagnosis, prognosis, and treatment planning; howeve…
Generative Aging of Brain Images with Diffeomorphic Registration
Jingru Fu, Antonios Tzortzakakis, José Barroso +3
Analyzing and predicting brain aging is essential for early prognosis and accurate diagnosis of cognitive diseases. The technique of neuroimaging, such as Magnetic Resonance Imagin…
A deep learning-based pipeline for error detection and quality control of brain MRI segmentation results
Irene Brusini, Daniel Ferreira Padilla, José Barroso +4
Brain MRI segmentation results should always undergo a quality control (QC) process, since automatic segmentation tools can be prone to errors. In this work, we propose two deep le…
The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study
Gustav Mårtensson, Daniel Ferreira, Tobias Granberg +22
Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in…
AVRA: Automatic Visual Ratings of Atrophy from MRI images using Recurrent Convolutional Neural Networks
Gustav Mårtensson, Daniel Ferreira, Lena Cavallin +4
Quantifying the degree of atrophy is done clinically by neuroradiologists following established visual rating scales. For these assessments to be reliable the rater requires substa…