Contributions to a conference proceedings

Focal Cortical Dysplasia (FCD) is a leading cause of drug-resistant focal epilepsy, and its identification on Magnetic Resonance Imaging (MRI) can enable potentially curative surgical treatment. FCD features are often subtle and heterogeneous and current deep learning models for automated detection exhibit limited sensitivity. [...]

Magnetic resonance imaging (MRI) inherently suffers from noise, which limits downstream medical analyses. In MRI, noise-free images are unobtainable; therefore, existing denoising approaches formulate surrogate training objectives, compromising between preserving detail and concealing noise, causing domain shifts or incomplete denoising. [...]

When navigating, humans and other species rely on landmarks and on self-motion cues to infer their position in space. It is suggested that in cue-integration processes, reliable cues are highly weighed, compared to less reliable ones. [...]

Experimental research on cognition needs more realistic paradigms to achieve ecological validity as wellas to transfer and eventually generalize the results to clinical practice. Selective attention was found to behighly related to memory and training of attentional filtering enhanced memory performance. [...]

The segmentation of brain tumors using magnetic resonance imaging (MRI) data is fundamental for precise diagnosis, effective treatment planning, and continuous monitoring of patient outcomes, and poses significant challenges as a result of variations in tumor morphology, intensity, variable size, and complex structure of tumor regions. Most existing deep learning approaches rely on task-specific architectures, requiring substantial effort to redesign and optimize models for each new imaging application. [...]

Magnetic resonance imaging (MRI) inherently suffers from noise, which limits downstream medical analyses. In MRI, noise-free images are unobtainable; therefore, existing denoising approaches formulate surrogate training objectives, compromising between preserving detail and concealing noise, causing domain shifts or incomplete de-noising. [...]

This study investigates how to improve the reliability of probability estimates produced by deep learning models for the detection of Alzheimer’s disease using MRI data. Although convolutional neural networks (CNNs) can accurately classify neurodegenerative diseases, their softmax outputs often misrepresent true classification probabilities. [...]

Accurately predicting age at symptom onset in genetic frontotemporal dementia (FTD) remains challenging due to substantial inter-individual variability, even among carriers of distinct pathogenic variants within the same gene. Time-to-event models provide a natural framework to address this problem while accounting for right censoring in presymptomatic individuals. [...]

Growing interest in perivascular spaces (PVS) quantification has highlighted the need for accurate and robust methods, particularly in magnetic resonance imaging (MRI). The Frangi filter is widely used to enhance tubular structures, including PVS, however its performance is highly dependent on scale selection. [...]

In many biomedical segmentation tasks, the preservation of elongated structure continuity and length is more important than voxel-wise accuracy. We propose two novel loss functions, Negative Centerline Loss and Simplified Topology Loss, that, applied to Convolutional Neural Networks (CNNs), help preserve connectivity of output instances. [...]