Deep unsupervised representation learning has recently led to new approaches in the field
of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these …

Brain tumors have become a severe medical complication in recent years due to their high
fatality rate. Radiologists segment the tumor manually, which is time-consuming, error …

Background and Objectives: Clinical diagnosis has become very significant in today's health
system. The most serious disease and the leading cause of mortality globally is brain cancer …

Gliomas are the most common malignant brain tumors with different grades that highly
determine the rate of survival in patients. Tumor segmentation and grading using magnetic …

Prominent solutions for medical image segmentation are typically tailored for automatic or
interactive setups posing challenges in facilitating progress achieved in one task to another …

Brain tumor segmentation is one of the most challenging problems in medical image
analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain …

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions
and relies on accurate segmentation of structures of interest. Deep learning-based …

Deep learning networks have recently been shown to outperform other segmentation
methods on various public, medical-image challenge datasets, particularly on metrics …

Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic
differences among a variety of cell populations. It paves the way to studying biological …

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural
Network for the challenging task of brain lesion segmentation. The devised architecture is …