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

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Eleni Kalantidou,Guy Keulemans,Alison Gillerior performance compared with mean pooling strategy in the traditional state-of-the-art coding methods such as sparse coding, linear locality-constrained coding and so on. However, the max pooling strategy in SPP-net only retains the strongest activated pattern, and would completely ignore the fre

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Designed Technologies for Healthy Aging is small relative to the dimensionality of the images. To cope with sparse voxel data, we propose utilizing the Euclidean distance transform (EDT) for increasing information density by populating each voxel with a distance value. To reduce the risk of overfitting resulting from high image dimension

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Vincent Jeanne,Maarten Bodlaenderep Neural Networks (DNNs) to address the DCE-MRI de-noising challenges. The key idea is based on an ensembling of expert DNNs, where each is trained for different noise characteristics and curve prototypes to solve an inverse problem on a specific subset of the input space. The most likely reconstru

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