Brain tumor, abnormal cell growth within the brain, require precise segmentation to facilitate
effective treatment planning. Accurately identifying tumor boundaries from complex Magnetic …

Image-to-image translation can create large impact in medical imaging, as images can be
synthetically transformed to other modalities, sequence types, higher resolutions or lower …

Recently, federated learning has raised increasing interest in the medical image analysis
field due to its ability to aggregate multi-center data with privacy-preserving properties. A …

Deformable image registration (DIR) is essential for medical image analysis, facilitating the
establishment of dense correspondences between images to analyze complex …

Brain metastases (BMs) are the most frequently occurring brain tumors. The treatment of
patients having multiple BMs with stereotactic radiosurgery necessitates accurate …

Despite significant advancements in automatic brain tumor segmentation methods, their
performance is not guaranteed when certain MR sequences are missing. Addressing this …

Abstract Magnetic Resonance Imaging (MRI) is essential for non-inasive brain tumor
detection, but accurately grading tumors is difficult due to variability in tumor types, sizes …

A critical challenge for tumour segmentation models is the ability to adapt to diverse clinical
settings, particularly when applied to poor quality neuroimaging data. The uncertainty …

Real-life medical data is often multimodal and incomplete, fueling the growing need for
advanced deep learning models capable of integrating them efficiently. The use of diverse …

Medical image segmentation is crucial in modern medical diagnostics, especially in
accurately locating and identifying brain tumors. However, current segmentation models are …