The renaissance of deep learning has provided promising solutions to various tasks. While
conventional deep learning models are constructed for a single specific task, multi-task deep …

Deep learning algorithms have greatly improved our ability to estimate eloquent cortex
regions from resting-state brain scans for patients about to undergo neurosurgery. The use …

Technological advances enable the design of systems that interact more closely with
humans in a multitude of previously unsuspected fields. Martial arts are not outside the …

Brain diseases threaten hundreds of thousands of people over the world. Medical imaging
techniques such as MRI and CT are employed for various brain disease studies. As artificial …

Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for
investigating the relationship between brain function and cognitive processes as it allows for …

Objective: Epileptogenic zone (EZ) localization is a crucial step during diagnostic work up
and therapeutic planning in medication refractory epilepsy. In this paper, we present the first …

Deep learning has demonstrated great potential for objective diagnosis of neuropsychiatric
disorders based on neuroimaging data, which includes the promising resting-state …

Multi-paradigm deep learning models show great potential for dynamic functional
connectivity (dFC) analysis by integrating complementary information. However, many of …

Connectomics is a neuroscience paradigm focused on noninvasively mapping highly
intricate and organized networks of neurons. The advent of neuroimaging has led to …

Background and objectives Tumor location and eloquence are two crucial preoperative
factors when deciding on the optimal treatment choice in glioma management. Consensus is …