The ability to capture inter-frame dynamics has been critical to the development of video
salient object detection (VSOD). While many works have achieved great success in this field …

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 …

This paper proposes an effective spatiotemporal saliency model for unconstrained videos
with complicated motion and complex scenes. First, superpixel-level motion and color …

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

Video salient object detection is a fundamental computer vision task aimed at highlighting
the most conspicuous objects in a video sequence. There are two key challenges presented …

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 …

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 …

Video salient object detection targets at extracting the most conspicuous objects in a video
sequence, which facilitate various video processing tasks, eg, video compression, video …