AIMC Topic: Neural Networks, Computer

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Deblurring Dynamic Scenes via Spatially Varying Recurrent Neural Networks.

IEEE transactions on pattern analysis and machine intelligence
Deblurring images captured in dynamic scenes is challenging as the motion blurs are spatially varying caused by camera shakes and object movements. In this paper, we propose a spatially varying neural network to deblur dynamic scenes. The proposed mo...

Deep Polynomial Neural Networks.

IEEE transactions on pattern analysis and machine intelligence
Deep convolutional neural networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of thei...

An Automatic System for Continuous Pain Intensity Monitoring Based on Analyzing Data from Uni-, Bi-, and Multi-Modality.

Sensors (Basel, Switzerland)
Pain is a reliable indicator of health issues; it affects patients' quality of life when not well managed. The current methods in the clinical application undergo biases and errors; moreover, such methods do not facilitate continuous pain monitoring....

An End-to-End Deep Learning Approach for State Recognition of Multifunction Radars.

Sensors (Basel, Switzerland)
With the widespread use of multifunction radars (MFRs), it is hard for the traditional radar signal recognition technology to meet the needs of current electronic intelligence systems. For signal recognition of an MFR, it is necessary to identify not...

Spectral pruning of fully connected layers.

Scientific reports
Training of neural networks can be reformulated in spectral space, by allowing eigenvalues and eigenvectors of the network to act as target of the optimization instead of the individual weights. Working in this setting, we show that the eigenvalues c...

A multi-level feature-fusion-based approach to breast histopathological image classification.

Biomedical physics & engineering express
Previously, convolutional neural networks mostly used deep semantic feature information obtained from several convolutions for image classification. Such deep semantic features have a larger receptive field, and the features extracted are more effect...

Improving Breast Tumor Segmentation in PET via Attentive Transformation Based Normalization.

IEEE journal of biomedical and health informatics
Positron Emission Tomography (PET) has become a preferred imaging modality for cancer diagnosis, radiotherapy planning, and treatment responses monitoring. Accurate and automatic tumor segmentation is the fundamental requirement for these clinical ap...

RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging.

IEEE journal of biomedical and health informatics
The retinal vasculature provides important clues in the diagnosis and monitoring of systemic diseases including hypertension and diabetes. The microvascular system is of primary involvement in such conditions, and the retina is the only anatomical si...

Self-Supervised Bi-Channel Transformer Networks for Computer-Aided Diagnosis.

IEEE journal of biomedical and health informatics
Self-supervised learning (SSL) can alleviate the issue of small sample size, which has shown its effectiveness for the computer-aided diagnosis (CAD) models. However, since the conventional SSL methods share the identical backbone in both the pretext...