Titlebook: Machine Learning in Medical Imaging; Third International Fei Wang,Dinggang Shen,Kenji Suzuki Conference proceedings 2012 Springer-Verlag B

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Conference proceedings 2012. The main aim of this workshop is to help advance the scientific research within the broad field of machine learning in medical imaging. It focuses on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging.

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Sune Darkner,Line H. Clemmensen to include learning of some aspects in depth, that is, Lifedeep learning. An understanding of the impact of technology, as a significant element in human learning beyond being operational tools, as Lifetech le978-3-031-68242-1978-3-031-68240-7Series ISSN 1871-322X Series E-ISSN 2730-5325

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Transductive Prostate Segmentation for CT Image Guided Radiotherapy, image. The final segmentation result is obtained by aligning the manually segmented prostate regions of the planning and previous treatment images, onto the estimated prostate-likelihood map of the current treatment image for majority voting. The proposed method has been evaluated on a real prostat

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MRI Confirmed Prostate Tissue Classification with Laplacian Eigenmaps of Ultrasound RF Spectra,ostate gland in both MRI and ultrasound. This method is developed to transfer the diagnostic references from MRI to US for training and validation of the proposed ultrasound-based prostate tissue classification technique. It yields a target registration error of 3.5±2.1 mm. We also report its use fo

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,Hierarchical Ensemble of Multi-level Classifiers for Diagnosis of Alzheimer’s Disease,ifiers are generated, with each evaluating the high-level features of different brain regions. Finally, all high-level classifiers are combined to make final decision. Our method is evaluated using MR brain images on 427 subjects (including 198 AD patients and 229 normal controls) from Alzheimer’s D