2,615 research outputs found
Isointense infant brain MRI segmentation with a dilated convolutional neural network
Quantitative analysis of brain MRI at the age of 6 months is difficult
because of the limited contrast between white matter and gray matter. In this
study, we use a dilated triplanar convolutional neural network in combination
with a non-dilated 3D convolutional neural network for the segmentation of
white matter, gray matter and cerebrospinal fluid in infant brain MR images, as
provided by the MICCAI grand challenge on 6-month infant brain MRI
segmentation.Comment: MICCAI grand challenge on 6-month infant brain MRI segmentatio
Visual Speech Recognition Using a 3D Convolutional Neural Network
Main stream automatic speech recognition (ASR) makes use of audio data to identify spoken words, however visual speech recognition (VSR) has recently been of increased interest to researchers. VSR is used when audio data is corrupted or missing entirely and also to further enhance the accuracy of audio-based ASR systems. In this research, we present both a framework for building 3D feature cubes of lip data from videos and a 3D convolutional neural network (CNN) architecture for performing classification on a dataset of 100 spoken words, recorded in an uncontrolled envi- ronment. Our 3D-CNN architecture achieves a testing accuracy of 64%, comparable with recent works, but using an input data size that is up to 75% smaller. Overall, our research shows that 3D-CNNs can be successful in finding spatial-temporal features using unsupervised feature extraction and are a suitable choice for VSR-based systems
Deep 3D Convolutional Neural Network for Automated Lung Cancer Diagnosis
Computer Aided Diagnosis has emerged as an indispensible technique for
validating the opinion of radiologists in CT interpretation. This paper
presents a deep 3D Convolutional Neural Network (CNN) architecture for
automated CT scan-based lung cancer detection system. It utilizes three
dimensional spatial information to learn highly discriminative 3 dimensional
features instead of 2D features like texture or geometric shape whick need to
be generated manually. The proposed deep learning method automatically extracts
the 3D features on the basis of spatio-temporal statistics.The developed model
is end-to-end and is able to predict malignancy of each voxel for given input
scan. Simulation results demonstrate the effectiveness of proposed 3D CNN
network for classification of lung nodule in-spite of limited computational
capabilities.Comment: Initial draft of PAPER Presented at IRSCNS 2018 , Goa , India final
version available at Mishra S., Chaudhary N.K., Asthana P., Kumar A. (2019)
Deep 3D Convolutional Neural Network for Automated Lung Cancer Diagnosis. In:
Peng SL., Dey N., Bundele M. (eds) Computing and Network Sustainability.
Lecture Notes in Networks and Systems, vol 75. Springer, Singapor
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