The semantic image segmentation task presents a trade-off between test time accuracy and
training time annotation cost. Detailed per-pixel annotations enable training accurate …

Deep convolutional neural networks (DCNNs) trained on a large number of images with
strong pixel-level annotations have recently significantly pushed the state-of-art in semantic …

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 …

Recently, significant improvement has been made on semantic object segmentation due to
the development of deep convolutional neural networks (DCNNs). Training such a DCNN …

Weakly-supervised learning based on, eg, partially labelled images or image-tags, is
currently attracting significant attention in CNN segmentation as it can mitigate the need for …

Accurate multi-organ abdominal CT segmentation is essential to many clinical applications
such as computer-aided intervention. As data annotation requires massive human labor …

In this paper, we propose an approach for semi-automatic annotation of object instances.
While most current methods treat object segmentation as a pixel-labeling problem, we here …

Existing semi-supervised learning (SSL) algorithms use a single weight to balance the loss
of labeled and unlabeled examples, ie, all unlabeled examples are equally weighted. But …

In digital pathology, tissue slides are scanned into Whole Slide Images (WSI) and
pathologists first screen for diagnostically-relevant Regions of Interest (ROIs) before …

Many problems in real-world applications involve predicting several random variables that
are statistically related. Markov random fields (MRFs) are a great mathematical tool to …