neural networks based segmentation
Segmentation of Lung Images using Region Based Neural Networks
... image segmentation. This is common withsome features such as color based, intensity based, or texture ...Computer based segmentation of lung CT images has been an important and ...
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A Review of Semantic Segmentation Using Deep Neural Networks
... One issue in FCN approaches is that by propagating through several alternated convolutional and pooling layers, the resolution of the output feature maps is down-sampled. Therefore, the direct predictions of FCN are ...
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Efficient Retinal Image Segmentation Using Wavelets and Neural Networks
... Wavelet-based texture analysis gives a multi determination analytical platform which enable us to characterize a signal (an image) in multiple spatial/frequency spaces. The multi-scale characteristics of wavelet ...
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Multilingual segmentation based on neural networks and pre trained word embeddings
... at each time step depending not only on the in- formation of the current input word, but of the already processed input. Contrary to other algo- rithms (perceptron (Afantenos et al., 2010)). Bi- LSTMs are a special case ...
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Detection of Glacier Calving Margins with Convolutional Neural Networks: A Case Study
... is based on semantic image segmentation using Convolutional Neural Networks (CNN) with a modified U-Net architecture to isolate the calving fronts from satellite images after having been ...
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Convolutional neural networks for brain tumour segmentation
... example segmentation process is devised. The segmentation process should be fully automated and in an ideal situation be performed in institutions with the same scanner/imaging protocols given discrepancies ...
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Lung Semantic Segmentation using Convolutional Neural Networks
... mentioned segmentation convolutional neural networks are applied on NIH dataset, an open dataset where research can be carried ...classified based on abnormalities or any cancer ...using ...
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THYROID SEGMENTATION IN US IMAGES USING NEURAL NETWORKS
... After division, highlights are removed either at the cell or at the tissue-level to quantify morphological attributes of picture for variation from the norm or to order the picture for distinctive evaluations of malady. ...
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An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images
... learning based segmentation methods have been widely used due to their high per- ...learning based methods to classify and distinguish nuclei from the background ...
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Applying deep matching networks to Chinese medical question answering: a study and a dataset
... was based on words and suffered from Chinese word segmentation failure in some ...convolutional neural network (CNN, [16]) for Chinese medical QA and released a dataset ...
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Supervoxel-based segmentation of 3D imagery with optical flow integration for spatiotemporal processing
... video segmentation methods using deep neural networks and also performed additional experiments on introduc- ing transfer learning to the 3D UCM ...art neural network, added to its original ...
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Brain Tumor Detection Using Neural Network
... of segmentation the accuracy of the algorithms is ...automatic segmentation method based on Convolutional Neural Networks (CNN) to overcome above ...tumor segmentation and ...
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Thyroid Segmentation and Volume Estimation Using CT Images
... spatio-temporal neural networks have been developed to characterize the time evolution time evolution properties of lesions after the injection of an administrative material ...Cognitron neural ...
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Road Segmentation on Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields
... There have been many approaches in road network extraction from very-high-resolution (VHR) aerial and satellite imagery literature. Wand et al. [14] proposed a DCNN and FSM (finite state machine)-based framework ...
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Convolutional Neural Networks in Application to Segmentation of Fingerprint Images
... Abstract. Segmentation of fingerprint images is one of the most important problems concerned with automatic biometric identification ...system. Segmentation is used to separate the area of the fingerprint ...
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BRAIN COMPUTER INTERFACE BASED ROBOT DESIGN
... classification, segmentation and performance ...Probabilistic Neural Networks, segmentation by K-means algorithm and performance analysis using confusion matrix ...
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Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in US images
... The second and third CNN architectures proposed in this study for breast lesion segmentation in US image, CNN2 and CNN3, are DAG architectures. A DAG archi- tecture has layers arranged as a directed acyclic graph. ...
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An agent based method for predicting monthly maximum & minimum quote prices
... artificial neural networks (ANNs), researchers and investors are hoping that the market mysteries can be ...artificial neural networks have been popularly applied to finance problems such as ...
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Dynamic visual cryptography with Arnolds logarithm using ANN for medical data protection
... Visual cryptography (VC), first designed in 1994 by Naor and Shamir [1], is a obscure sharing arrangement, based on black and- white or binary images. Secret images are split into share images which, on their own, ...
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Is Word Segmentation Necessary for Deep Learning of Chinese Representations?
... word segmentation does not pro- vide any additional ...only based on characters, and thus do not suffer from the data sparsity issue, OOV issue and the overfitting issue of the word-based ...word ...
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