Training robust deep learning (DL) systems for medical image classification or segmentation
is challenging due to limited images covering different disease types and severity. We …

In supervised learning for medical image analysis, sample selection methodologies are
fundamental to attain optimum system performance promptly and with minimal expert …

Although generative adversarial network (GAN) based style transfer is state of the art in
histopathology color-stain normalization, they do not explicitly integrate structural …

The success of deep learning has set new benchmarks for many medical image analysis
tasks. However, deep models often fail to generalize in the presence of distribution shifts …

Medical image segmentation is important for computer aided diagnosis. Pixelwise manual
annotations of large datasets require high expertise and is time consuming. Conventional …

We propose an information processing pipeline for segmenting parts of the bowel in
abdominal magnetic resonance images that are affected with Crohn's disease. Given a …

Training robust deep learning (DL) systems for disease detection from medical images is
challenging due to limited images covering different disease types and severity. The …

Extracting moving and salient objects from videos is important for many applications like
surveillance and video retargeting. In this paper we use spatial and temporal coherency …

Increasing incidence of Crohn's disease (CD) in the Western world has made its accurate
diagnosis an important medical challenge. The current reference standard for diagnosis …

We propose a fully automated method for segmenting the cardiac right ventricle (RV) from
magnetic resonance (MR) images. Given a MR test image, it is first oversegmented into …