AIMC Topic: Neural Networks, Computer

Clear Filters Showing 23741 to 23750 of 31376 articles

Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition.

IEEE transactions on pattern analysis and machine intelligence
Heterogeneous face recognition (HFR) aims at matching facial images acquired from different sensing modalities with mission-critical applications in forensics, security and commercial sectors. However, HFR presents more challenging issues than tradit...

DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation.

IEEE transactions on pattern analysis and machine intelligence
Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may ...

Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training.

IEEE transactions on medical imaging
To realize the full potential of deep learning for medical imaging, large annotated datasets are required for training. Such datasets are difficult to acquire due to privacy issues, lack of experts available for annotation, underrepresentation of rar...

Assessing functional connectivity across 3D tissue engineered axonal tracts using calcium fluorescence imaging.

Journal of neural engineering
OBJECTIVE: Micro-tissue engineered neural networks (micro-TENNs) are anatomically-inspired constructs designed to structurally and functionally emulate white matter pathways in the brain. These 3D neural networks feature long axonal tracts spanning d...

Patient Similarity Networks for Precision Medicine.

Journal of molecular biology
Clinical research and practice in the 21st century is poised to be transformed by analysis of computable electronic medical records and population-level genome-scale patient profiles. Genomic data capture genetic and environmental state, providing in...

Heart Sound Segmentation-An Event Detection Approach Using Deep Recurrent Neural Networks.

IEEE transactions on bio-medical engineering
OBJECTIVE: In this paper, we accurately detect the state-sequence first heart sound (S1)-systole-second heart sound (S2)-diastole, i.e., the positions of S1 and S2, in heart sound recordings. We propose an event detection approach without explicitly ...

Estimating Brain Connectivity With Varying-Length Time Lags Using a Recurrent Neural Network.

IEEE transactions on bio-medical engineering
OBJECTIVE: Computer-aided estimation of brain connectivity aims to reveal information propagation in brain automatically, which has great potential in clinical applications, e.g., epilepsy foci diagnosis. Granger causality is an effective tool for di...

Representation learning using event-based STDP.

Neural networks : the official journal of the International Neural Network Society
Although representation learning methods developed within the framework of traditional neural networks are relatively mature, developing a spiking representation model remains a challenging problem. This paper proposes an event-based method to train ...

Adaptive neural output-feedback control for nonstrict-feedback time-delay fractional-order systems with output constraints and actuator nonlinearities.

Neural networks : the official journal of the International Neural Network Society
This study addresses the issue of the adaptive output tracking control for a category of uncertain nonstrict-feedback delayed incommensurate fractional-order systems in the presence of nonaffine structures, unmeasured pseudo-states, unknown control d...

A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy.

Medical image analysis
Targeted prostate biopsy, incorporating multi-parametric magnetic resonance imaging (mp-MRI) and its registration with ultrasound, is currently the state-of-the-art in prostate cancer diagnosis. The registration process in most targeted biopsy system...