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Titlebook: Understanding and Interpreting Machine Learning in Medical Image Computing Applications; First International Danail Stoyanov,Zeike Taylor,

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Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessmepredicting tumor grade directly from imaging data. In this way, we overcome the need for expert annotations of regions of interest. We evaluate two prediction approaches: from the whole brain, and from an automatically defined tumor region. Finally, we employ interpretability methodologies as a qual
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How Users Perceive Content-Based Image Retrieval for Identifying Skin Imagesption of confidence and trust. Our study with 16 novice users for a given set of annotated dermoscopy images indicates that, in general, CBIR enables novices to make a significantly more accurate classification on a new skin lesion image from four commonly-observed categories: Nevus, Seborrheic Kera
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e oxygen species (ROS) that mediate programmed death of cells (apoptosis and necrosis) and organisms (phenoptosis). The latter process is considered as a key mechanism of aging which may be suppressed by mitochondria-targeted antioxidants.978-3-642-43582-9978-3-642-33430-6
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Clement Abi Nader,Nicholas Ayache,Philippe Robert,Marco Lorenzi,for the Alzheimer’s Disease Neuroima the impression that the principles of biological control can be understood only after one has undergone a rather high-powered course in elec­ tronic control theory. It often seems to be assumed that it is electronics which must do all the teaching while biology and medicine must do all the learning
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James R. Clough,Daniel R. Balfour,Claudia Prieto,Andrew J. Reader,Paul K. Marsden,Andrew P. Kingarch and treatment that will afford greater opportunities for a patient’s personalized cancer treatment. This was first envisioned in the 1987 initial edition of this textbook and is now a “new” and popular approach to cancer treatment. Some forms of cancer biotherapy use the strategy of tumor stabi
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