Accurate medical image segmentation is essential for diagnosis, surgical planning and
many other applications. Convolutional Neural Networks (CNNs) have become the state-of …

We introduce a new loss function for the weakly-supervised training of semantic image
segmentation models based on three guiding principles: to seed with weak localization …

Most state-of-the-art techniques for multi-class image segmentation and labeling use
conditional random fields defined over pixels or image regions. While regionlevel models …

Multi-atlas segmentation is an effective approach for automatically labeling objects of
interest in biomedical images. In this approach, multiple expert-segmented example images …

Weakly supervised semantic segmentation and localization have a problem of focusing only
on the most important parts of an image since they use only image-level annotations. In this …

Due to the development of the computer vision, machine learning, and deep learning
technologies, the research community focuses not only on the traditional SLAM problems …

In this paper, a two-step automatic change detection chain for rapid flood mapping based on
Sentinel-1 Synthetic Aperture Radar (SAR) data is presented. First, a reference image is …

Markov and Conditional random fields (crf s) used in computer vision typically model only
local interactions between variables, as this is computationally tractable. In this paper we …

Likelihood-based learning of graphical models faces challenges of computational
complexity and robustness to model misspecification. This paper studies methods that fit …

Automated segmentation of brain glioma plays an active role in diagnosis decision,
progression monitoring and surgery planning. Based on deep neural networks, previous …