Disease diagnosis and monitoring using conventional healthcare services is typically
expensive and has limited accuracy. Wearable health technology based on flexible …

This review delves into the most recent advancements in applying artificial intelligence (AI)
within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors …

Brain tumor segmentation in multimodal MRI has great significance in clinical diagnosis and
treatment. The utilization of multimodal information plays a crucial role in brain tumor …

Simple Summary In this research, we addressed the challenging task of brain tumor
detection in MRI scans using a large collection of brain tumor images. We demonstrated that …

While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by
supporting and contributing to the evidence-based practice of medicine, personalizing …

Semantic segmentation of brain tumors is a fundamental medical image analysis task
involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient …

Although machine learning (ML) has shown promise across disciplines, out-of-sample
generalizability is concerning. This is currently addressed by sharing multi-site data, but …

The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the
Radiological Society of North America (RSNA), the American Society of Neuroradiology …

U-Net has become a cornerstone in various visual applications such as image segmentation
and diffusion probability models. While numerous innovative designs and improvements …