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 …

The successful early diagnosis of brain tumors plays a major role in improving the treatment
outcomes and thus improving patient survival. Manually evaluating the numerous magnetic …

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 …

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 …

A cascade of fully convolutional neural networks is proposed to segment multi-modal
Magnetic Resonance (MR) images with brain tumor into background and three hierarchical …

Among brain tumors, gliomas are the most common and aggressive, leading to a very short
life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the …

Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis,
treatment planning, and treatment outcome evaluation. Build upon successful deep learning …

Automatic segmentation of brain tumors from medical images is important for clinical
assessment and treatment planning of brain tumors. Recent years have seen an increasing …

In this paper we report the set-up and results of the Multimodal Brain Tumor Image
Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and …

Tumor in brain is an anthology of anomalous cells. It leads to increase in death rate among
humans. Therefore, in this manuscript, a fusion process is proposed to combine structural …