Abstract: Brain tumor segmentation, Delineating tumor areas from a fin-n-healthy brain tissue in medical pictures is critical for proper diagnosis, planning of treatment, and observing alignment ...

Abstract: Accurate liver and tumor segmentation in CT images is vital for diagnosis and treatment planning. This study presents SegResNet_2335, a lightweight 3D residual network optimized for ...

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 ...

First, we pretrained the encoder of a transformer-based network using a self-supervised approach on unlabeled abdominal computed tomography images. Subsequently, we fine-tuned the segmentation network ...

Medical image segmentation plays a vital role in diagnostic imaging, particularly for measuring brain tumor morphology in MRI scans, which directly influences treatment planning, prognosis, and ...

The NCCN guidelines now include MammaPrint for identifying patients with HR+/HER2– early-stage breast cancer who may benefit from anthracycline-based chemotherapy. The FLEX study provided evidence ...

ABSTRACT: Liver cancer is one of the most prevalent and lethal forms of cancer, making early detection crucial for effective treatment. This paper introduces a novel approach for automated liver tumor ...