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

BRAWL

制造

STEER

Conference proceedings 2016ge reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques..

Range-Of-Motion

0302-9743 medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques..978-3-319-46975-1978-3-319-46976-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

摻假

HEp-2 Cell Classification Using K-Support Spatial Pooling in Deep CNNserior 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

obeisance

Fast Predictive Image Registrationetwork, which allows evaluation of deformation field uncertainty through Monte Carlo sampling using dropout at test time. We show that deformation uncertainty highlights areas of ambiguous deformations. We test our method on the OASIS brain image dataset in 2D and 3D.

為寵愛

阻撓

De-noising of Contrast-Enhanced MRI Sequences by an Ensemble of Expert Deep Neural Networksep 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

免除責(zé)任

Three-Dimensional CT Image Segmentation by Combining 2D Fully Convolutional Network with 3D Majorityt any adjustment. We applied the proposed method to segment a wide range of anatomical structures that consisted of 19 types of targets in the human torso, including all the major organs. A database consisting of 240 3D CT scans and a humanly annotated ground truth was used for training and testing.

同步左右

Medical Image Description Using Multi-task-loss CNNates the need for hand-crafted features, and allows application of the method to new modalities and organs with minimal overhead. The proposed approach generates medical report by estimating standard radiological lexicon descriptors which are a basis for diagnosis. The proposed approach should help