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Microcalcifications are an early mammographic indicator of breast cancer. To assist screening radiologists in reading mammograms, machine learning techniques have been developed for the automated detection of microcalcifications. In the last few years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision and medical image analysis applications. A...
Assessment of retinal vessels is fundamental for the diagnosis of many disorders such as heart diseases, diabetes and hypertension. The imaging of retina using advanced fundus camera has become a standard in computer-assisted diagnosis of opthalmic disorders. Modern cameras produce high quality color digital images, but during the acquisition process the light reflected by the retinal surface generates...
Modern high-throughput bioimaging techniques pose the unprecedented challenge of exploring and analyzing the produced Terabyte-scale volumetric images directly in their 3D space. Without expensive virtual reality devices and/or parallel computing infrastructures, this becomes even more demanding and calls for new, more scalable tools that help exploring these very large 3D data also on common laptops...
Scientific problems dealing with the processing of large amounts of data require efforts in the integration of proper services and applications to facilitate the research activity, interacting with high performance computing resources. Easier access to these resources have a profound impact on research in neuroscience, leading to advances in the management and processing of neuro-anatomical images...
In this paper we present a cascade-based framework for object detection in which the node classifiers are trained by a learning algorithm based on ranking instead of classification error. Such an approach is particularly suited for facing the asymmetry between positive and negative class, that is a huge problem in object detection applications. Other methods focused on this problem and previously...
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