AIMC Topic: Pattern Recognition, Automated
Clear Filters Showing 591 to 600 of 1671 articles
Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks.
International journal of computer assisted radiology and surgery
Apr 30, 2019
PURPOSE: We address the automatic segmentation of healthy and cancerous liver tissues (parenchyma, active and necrotic parts of hepatocellular carcinoma (HCC) tumor) on multiphase CT images using a deep learning approach.
Segmenting and classifying activities in robot-assisted surgery with recurrent neural networks.
International journal of computer assisted radiology and surgery
Apr 29, 2019
PURPOSE: Automatically segmenting and classifying surgical activities is an important prerequisite to providing automated, targeted assessment and feedback during surgical training. Prior work has focused almost exclusively on recognizing gestures, o...
Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition.
IEEE transactions on cybernetics
Apr 24, 2019
Brain-computer interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG)-based BCI is one of the promising solutions due to its convenient and portab...
Using Surgeon Hand Motions to Predict Surgical Maneuvers.
Human factors
Apr 23, 2019
OBJECTIVE: This study explores how common machine learning techniques can predict surgical maneuvers from a continuous video record of surgical benchtop simulations.
Photomontage detection using steganography technique based on a neural network.
Neural networks : the official journal of the International Neural Network Society
Apr 17, 2019
This article presents a steganographic method StegoNN based on neural networks. The method is able to identify a photomontage from presented signed images. Unlike other academic approaches using neural networks primarily as classifiers, the StegoNN m...
Hypercomplex extreme learning machine with its application in multispectral palmprint recognition.
PloS one
Apr 15, 2019
An extreme learning machine (ELM) is a novel training method for single-hidden layer feedforward neural networks (SLFNs) in which the hidden nodes are randomly assigned and fixed without iterative tuning. ELMs have earned widespread global interest d...
Dense networks with relative location awareness for thorax disease identification.
Medical physics
Apr 14, 2019
PURPOSE: Chest X-ray is one of the most common examinations for diagnosing heart and lung diseases. Due to the existing of a large number of clinical cases, many automated diagnosis algorithms based on chest X-ray images have been proposed. To our kn...
Improving the Antinoise Ability of DNNs via a Bio-Inspired Noise Adaptive Activation Function Rand Softplus.
Neural computation
Apr 12, 2019
Although deep neural networks (DNNs) have led to many remarkable results in cognitive tasks, they are still far from catching up with human-level cognition in antinoise capability. New research indicates how brittle and susceptible current models are...
Information Transmitted From Bioinspired Neuron-Astrocyte Network Improves Cortical Spiking Network's Pattern Recognition Performance.
IEEE transactions on neural networks and learning systems
Apr 11, 2019
We trained two spiking neural networks (SNNs), the cortical spiking network (CSN) and the cortical neuron-astrocyte network (CNAN), using a spike-based unsupervised method, on the MNIST and alpha-digit data sets and achieve an accuracy of 96.1% and 7...
TSE-CNN: A Two-Stage End-to-End CNN for Human Activity Recognition.
IEEE journal of biomedical and health informatics
Apr 9, 2019
Human activity recognition has been widely used in healthcare applications such as elderly monitoring, exercise supervision, and rehabilitation monitoring. Compared with other approaches, sensor-based wearable human activity recognition is less affec...
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