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), ...

Additional visualizations highlighting the comparison between the proposed two-stage AG-VQ-VAE network (without skip connections) and the single-stage AG-UNet (with skip connections) are presented.

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 ...

GLP-1 receptor agonists show promise in treating various chronic conditions, but potential risks and unanswered questions remain for care of patients with NETs. In recent years, the clinical use of ...

Abstract: The proposed work focuses on using LadderNet for Brain Tumor segmentation using MRI signals through the dataset as an input. The method is helpful in computerized medical analysis. Although ...

1 Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL, USA. 2 Department of Mathematics and Computer Science, Islamic Azad University, Science and Research Branch, Tehran, ...

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 ...

SAN DIEGO, July 30, 2025 /PRNewswire/ -- Cortechs.ai, a pioneer in AI-powered medical imaging solutions, is pleased to announce the immediate availability of its latest release of the FDA-cleared ...

Abstract: The present study offers a convolutional neural network (CNN) architecture with tumor segmentation using a hybrid CNN & LSTM model. The study makes use of a split dataset, where the training ...