Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming
their negative repercussions is considered a hurdle in biomedical imaging. The combination …

Abstract The Segment Anything Model (SAM), a foundation model for general image
segmentation, has demonstrated impressive zero-shot performance across numerous …

Abstract Large Vision-Language Models (LVLMs) have demonstrated remarkable
capabilities in various multimodal tasks. However their potential in the medical domain …

Segment Anything Model (SAM) has achieved impressive results for natural image
segmentation with input prompts such as points and bounding boxes. Its success largely …

Multiple Sclerosis (MS) is an autoimmune disease that causes brain and spinal cord lesions,
which magnetic resonance imaging (MRI) can detect and characterize. Recently, deep …

Automated segmentation of brain white matter lesions is crucial for both clinical assessment
and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an …

In this study, we introduce “instance loss functions”, a new family of loss functions designed
to enhance the training of neural networks in the instance-level segmentation and detection …

Surgery performed by a novice neurosurgeon under constant supervision of a senior
surgeon with the experience of thousands of operations, able to handle any intraoperative …

Purpose Recent advances in magnetic resonance (MR) scanner quality and the rapidly
improving nature of facial recognition software have necessitated the introduction of MR …

T2 lesion quantification plays a crucial role in monitoring disease progression and
evaluating treatment response in multiple sclerosis (MS). We developed a 3D, multi-arm U …