AIMC Topic: Neural Networks, Computer

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Respiratory signal prediction based on adaptive boosting and multi-layer perceptron neural network.

Physics in medicine and biology
To improve the prediction accuracy of respiratory signals using adaptive boosting and multi-layer perceptron neural network (ADMLP-NN) for gated treatment of moving target in radiation therapy. The respiratory signals acquired using a real-time posit...

SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs.

PloS one
Medical image collections contain a wealth of information which can assist radiologists and medical experts in diagnosis and disease detection for making well-informed decisions. However, this objective can only be realized if efficient access is pro...

Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences.

Medical image analysis
Dynamic contrast-enhanced MRI (DCE-MRI) is an imaging protocol where MRI scans are acquired repetitively throughout the injection of a contrast agent. The analysis of dynamic scans is widely used for the detection and quantification of blood-brain ba...

DeepSkeleton: Learning Multi-Task Scale-Associated Deep Side Outputs for Object Skeleton Extraction in Natural Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Object skeletons are useful for object representation and object detection. They are complementary to the object contour, and provide extra information, such as how object scale (thickness) varies among object parts. But object skeleton extraction fr...

Two-Stream Transformer Networks for Video-Based Face Alignment.

IEEE transactions on pattern analysis and machine intelligence
In this paper, we propose a two-stream transformer networks (TSTN) approach for video-based face alignment. Unlike conventional image-based face alignment approaches which cannot explicitly model the temporal dependency in videos and motivated by the...

Discriminative clustering on manifold for adaptive transductive classification.

Neural networks : the official journal of the International Neural Network Society
In this paper, we mainly propose a novel adaptive transductive label propagation approach by joint discriminative clustering on manifolds for representing and classifying high-dimensional data. Our framework seamlessly combines the unsupervised manif...

Root cause investigation of deviations in protein chromatography based on mechanistic models and artificial neural networks.

Journal of chromatography. A
In protein chromatography, process variations, such as aging of column or process errors, can result in deviations of the product and impurity levels. Consequently, the process performance described by purity, yield, or production rate may decrease. ...

Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: Delineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it is challenging for accurate and automatic segmentation of thyroid nodul...

Stochastic separation theorems.

Neural networks : the official journal of the International Neural Network Society
The problem of non-iterative one-shot and non-destructive correction of unavoidable mistakes arises in all Artificial Intelligence applications in the real world. Its solution requires robust separation of samples with errors from samples where the s...

DropConnected neural networks trained on time-frequency and inter-beat features for classifying heart sounds.

Physiological measurement
OBJECTIVE: Automatic heart sound analysis has the potential to improve the diagnosis of valvular heart diseases in the primary care phase, as well as in countries where there is neither the expertise nor the equipment to perform echocardiograms. An a...