Data augmentation is a popular technique which helps improve generalization capabilities
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …

The introduction of quantitative image analysis has given rise to fields such as radiomics
which have been used to predict clinical sequelae. One growing area of interest for analysis …

The automatic segmentation of brain tumors in Magnetic Resonance Imaging (MRI) plays a
major role in accurate diagnosis and treatment planning. The present study proposes a new …

One of the most crucial tasks in any brain tumor detection system is the isolation of abnormal
tissues from normal brain tissues. Interestingly, domain of brain tumor analysis has …

A brain tumor is one of the foremost reasons for the rise in mortality among children and
adults. A brain tumor is a mass of tissue that propagates out of control of the normal forces …

Gliomas are the most common and threatening brain tumors with little to no survival rate.
Accurate detection of such tumors is crucial for survival of the subject. Naturally, tumors have …

A brain tumor is the growth of abnormal cells in certain brain tissues with a high mortality
rate; therefore, it requires high precision in diagnosis, as a minor human judgment can …

The aim of this study was to computationally model, in an unsupervised manner, a manifold
of symmetry variations in normal brains, such that the learned manifold can be used to …

Accurate brain tumor segmentation from 3D Magnetic Resonance Imaging (3D-MRI) is an
important method for obtaining information required for diagnosis and disease therapy …

Purpose Accurately segmenting head and neck cancer (HNC) tumors in medical images is
crucial for effective treatment planning. However, current methods for HNC segmentation are …