Brain tumors pose a major challenge in neuro-oncology due to their high mortality rates and complex diagnosis. This review summarizes recent advances in using artificial intelligence (AI), ...
The human brain is complex. Artificial intelligence (AI) machine learning and medical imaging data are accelerating breakthroughs in brain health, especially in medical diagnostics. A peer-reviewed ...
As shown below, the inferred masks predicted by our segmentation model trained by the dataset appear similar to the ground truth masks. This repository contains a curated and enhanced version of brain ...
Focal cortical dysplasia (FCD) and dysembryoplastic neuroepithelial tumor (DNET) are two major causes of intractable epilepsy, often with confusing imaging findings. This study aimed to develop ...
University of Michigan researchers published a study in September, revealing that brain tumors rewire the use of glucose to fuel growth. By removing a specific amino acid from a patient’s diet, tumor ...
ABSTRACT: Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving ...
Abstract: Medical image segmentation is a critical task in clinical diagnosis and treatment, particularly for brain tumor analysis using imaging modalities such as Magnetic Resonance Imaging (MRI) and ...
Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...
Accurate characterization of glioma is essential for effective clinical decision-making. Most current studies involve a limited number of patients and focus solely on single-gene tasks. This research ...
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