Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …

In computer vision, edge and object contour detection is essential for higher-level vision
tasks, such as shape matching, visual salience, image segmentation, and object recognition …

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 …

Current state-of-the-art methods for image segmentation form a dense image representation
where the color, shape and texture information are all processed together inside a deep …

Tremendous efforts have been made to improve mask localization accuracy in instance
segmentation. Modern instance segmentation methods relying on fully convolutional …

We present a model-agnostic post-processing scheme to improve the boundary quality for
the segmentation result that is generated by any existing segmentation model. Motivated by …

Image classification systems recently made a giant leap with the advancement of deep
neural networks. However, these systems require an excessive amount of labeled data to be …

Annotating images with pixel-wise labels is a time-consuming and costly process. Recently,
DatasetGAN showcased a promising alternative-to synthesize a large labeled dataset via a …

Manually labeling objects by tracing their boundaries is a laborious process. In Polygon-
RNN++, the authors proposed Polygon-RNN that produces polygonal annotations in a …

Semantic segmentation and semantic edge detection can be seen as two dual problems
with close relationships in computer vision. Despite the fast evolution of learning-based 3D …