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An important initial step in many medical image analysis applications is the accurate detection of anatomical landmarks. Most successful methods for this task rely on data-driven machine learning algorithms. However, modern machine learning techniques, e.g. convolutional neural networks, need a large corpus of training data, which is often an unrealistic setting for medical datasets. In this work,...
The determination of an individual's legal majority age is becoming increasingly important in forensic practice. Established age estimation methods are based on 2D X-rays, but suffer from problems due to projective imaging and exposure to ionizing radiation, which, without proper medical or criminal indication, is ethically questionable and legally prohibited in many countries. We propose an automatic...
Semi-supervised learning has recently demonstrated be successful in large scale learning for image classification tasks. Laplacian Support Vector Machines (LapSVM) is one of such approaches applied to this task. However, LapSVM uses a squared hinge loss function for the labeled examples, which is not twice differentiable and may penalize noisy labeled examples too much. Thus, the accuracy decreases...
We propose an approach which allows to localize anatomical landmarks in radiological datasets given only a single manual annotation and set of un-annotated example images. Using top-down image patch regression to obtain potential landmark candidates in the set of training images, a model of the anatomical structure is incrementally enlarged, starting from the single, annotated image, until it encompasses...
In this paper we propose a simple and effective way to integrate structural information in random forests for semantic image labelling. By structural information we refer to the inherently available, topological distribution of object classes in a given image. Different object class labels will not be randomly distributed over an image but usually form coherently labelled regions. In this work we...
Online learning has shown to be successful in tracking of previously unknown objects. However, most approaches are limited to a bounding-box representation with fixed aspect ratio. Thus, they provide a less accurate foreground/background separation and cannot handle highly non-rigid and articulated objects. This, in turn, increases the amount of noise introduced during online self-training.
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