Multimodal fusion in neuroimaging combines data from multiple imaging modalities to
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …

What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

Applications that generate huge amounts of data in the form of fast streams are becoming
increasingly prevalent, being therefore necessary to learn in an online manner. These …

In recent years, significant progress has been made in developing more accurate and
efficient machine learning algorithms for segmentation of medical and natural images. In this …

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that
primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate …

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 …

MRI-based medical image analysis for brain tumor studies is gaining attention in recent
times due to an increased need for efficient and objective evaluation of large amounts of …

A brain tumor is an abnormal enlargement of cells if not properly diagnosed. Early detection
of a brain tumor is critical for clinical practice and survival rates. Brain tumors arise in a …

Computer-aided detection/diagnosis (CAD) systems can enhance the diagnostic capabilities
of physicians and reduce the time required for accurate diagnosis. The objective of this …

Image processing techniques being crucial towards analyzing and resolving issues in
medical imaging since last two decades. Medical imaging is a process or technique to find …