AIMC Topic: Deep Learning

Clear Filters Showing 23521 to 23530 of 28423 articles

Personalized Age Progression with Bi-Level Aging Dictionary Learning.

IEEE transactions on pattern analysis and machine intelligence
Age progression is defined as aesthetically re-rendering the aging face at any future age for an individual face. In this work, we aim to automatically render aging faces in a personalized way. Basically, for each age group, we learn an aging diction...

Automatic Segmentation of Kidneys using Deep Learning for Total Kidney Volume Quantification in Autosomal Dominant Polycystic Kidney Disease.

Scientific reports
Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most common inherited disorder of the kidneys. It is characterized by enlargement of the kidneys caused by progressive development of renal cysts, and thus assessment of total kidney volume ...

Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework.

Scientific reports
Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lac...

Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
This paper presents a deep-learning-based CADx for the differential diagnosis of embryonal (ERMS) and alveolar (ARMS) subtypes of rhabdomysarcoma (RMS) solely by analyzing multiparametric MR images. We formulated an automated pipeline that creates a ...

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.

NeuroImage
This study investigates a 3D and fully convolutional neural network (CNN) for subcortical brain structure segmentation in MRI. 3D CNN architectures have been generally avoided due to their computational and memory requirements during inference. We ad...

Deeply Learned View-Invariant Features for Cross-View Action Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Classifying human actions from varied views is challenging due to huge data variations in different views. The key to this problem is to learn discriminative view-invariant features robust to view variations. In this paper, we address this problem by...

VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images.

NeuroImage
Segmentation of key brain tissues from 3D medical images is of great significance for brain disease diagnosis, progression assessment and monitoring of neurologic conditions. While manual segmentation is time-consuming, laborious, and subjective, aut...

Towards automatic pulmonary nodule management in lung cancer screening with deep learning.

Scientific reports
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines...

Mobile Stride Length Estimation With Deep Convolutional Neural Networks.

IEEE journal of biomedical and health informatics
OBJECTIVE: Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-of-the...

DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

NeuroImage
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstra...