Titlebook: Deep Learning and Data Labeling for Medical Applications; First International Gustavo Carneiro,Diana Mateus,Julien Cornebise Conference pr

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https://doi.org/10.1007/978-3-031-01598-4ing early diagnosis of brain diseases. However, 7T MRI scanners are less accessible, compared to the 3T MRI scanners. This motivates us to reconstruct 7T-like images from 3T MRI. We propose a deep architecture for Convolutional Neural Network (CNN), which uses the . (intensity) and . (labels of brai

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Designed Technologies for Healthy Agingwork which learns the pixel/voxel-wise mapping between image appearance and registration parameters. Our approach can predict general deformation parameterizations, however, we focus on the large deformation diffeomorphic metric mapping (LDDMM) registration model. By predicting the LDDMM momentum-pa

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https://doi.org/10.1007/978-3-031-01598-4dness. Problematically, the visual indicators of ROP are not well understood and neonatal fundus images are usually of poor quality and resolution. We investigate two ways to aid clinicians in ROP detection using convolutional neural networks (CNN): (1) We fine-tune a pretrained GoogLeNet as a ROP d

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Vincent Jeanne,Maarten BodlaenderCT) examinations. FCN has proven to be a very powerful tool for semantic segmentation. We explore the FCN performance on a relatively small dataset and compare it to patch based CNN and sparsity based classification schemes. Our data contains CT examinations from 20 patients with overall 68 lesions

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Theoretical Approach in Design Methodologyent a new multi-task convolutional neural network (CNN) approach for detection and semantic description of lesions in diagnostic images. The proposed CNN-based architecture is trained to generate and rank rectangular regions of interests (ROI’s) surrounding suspicious areas. The highest score candid

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