Classifying shoulder implants in X-ray images using deep learning

Using deep learning on X-ray images to detect threats

Learning to Classify X-Ray Images Using Relational Learning

Image understanding often requires extensive background knowledge. The problem addressed in this paper is such knowledge can be acquired. We discuss how relational machine learning methods can be used to automatically build rules for classifying types of blood vessels. We introduce a new learning system that can make use of background knowledge coded as arbitrarily complex Prolog programs to co...

Classifying Gray-scale Sar Images: a Deep Learning Approach

Classifying Gray-scale differencing SAR images into two classes is very difficult due to the changeable impacts caused by the season, the imaging condition and so on. To optimize the state-of-the-art algorithms and to deal with the mentioned difficulty, a novel unsupervised classification algorithm is proposed based on deep learning, where the complex correspondence among the images is built up...

Retrieving and Classifying Affective Images via Deep Metric Learning

Affective image understanding has been extensively studied in the last decade since more and more users express emotion via visual contents. While current algorithms based on convolutional neural networks aim to distinguish emotional categories in a discrete label space, the task is inherently ambiguous. This is mainly because emotional labels with the same polarity (i.e., positive or negative)...

Classifying and segmenting microscopy images with deep multiple instance learning

MOTIVATION High-content screening (HCS) technologies have enabled large scale imaging experiments for studying cell biology and for drug screening. These systems produce hundreds of thousands of microscopy images per day and their utility depends on automated image analysis. Recently, deep learning approaches that learn feature representations directly from pixel intensity values have dominated...