AIMC Topic: Algorithms

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Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition.

IEEE transactions on cybernetics
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...

Image Quality Improvement of Hand-Held Ultrasound Devices With a Two-Stage Generative Adversarial Network.

IEEE transactions on bio-medical engineering
As a widely used imaging modality in the medical field, ultrasound has been applied in community medicine, rural medicine, and even telemedicine in recent years. Therefore, the development of portable ultrasound devices has become a popular research ...

Phy-PMRFI: Phylogeny-Aware Prediction of Metagenomic Functions Using Random Forest Feature Importance.

IEEE transactions on nanobioscience
High-throughput sequencing techniques have accelerated functional metagenomics studies through the generation of large volumes of omics data. The integration of these data using computational approaches is potentially useful for predicting metagenomi...

Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine Interface.

Sensors (Basel, Switzerland)
Everyday, people interact with different types of human machine interfaces, and the use of them is increasing, thus, it is necessary to design interfaces which are capable of responding in an intelligent, natural, inexpensive, and accessible way, reg...

Augmentation of CBCT Reconstructed From Under-Sampled Projections Using Deep Learning.

IEEE transactions on medical imaging
Edges tend to be over-smoothed in total variation (TV) regularized under-sampled images. In this paper, symmetric residual convolutional neural network (SR-CNN), a deep learning based model, was proposed to enhance the sharpness of edges and detailed...

Machine learning technology in the application of genome analysis: A systematic review.

Gene
Machine learning (ML) is a powerful technique to tackle many problems in data mining and predictive analytics. We believe that ML will be of considerable potentials in the field of bioinformatics since the high-throughput technology is producing ever...

Multimodal brain tumor image segmentation using WRN-PPNet.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Tumor segmentation is of great importance for diagnosis and prognosis of brain cancer in medical field. Because of the noise, inhomogeneous gray, diversity of tissue, bias among modalities, and the fuzzy boundaries between tumor and adjacent tissues ...

Brain Tumor Segmentation Based on Improved Convolutional Neural Network in Combination with Non-quantifiable Local Texture Feature.

Journal of medical systems
Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis. According to deep learning model, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNN) and dense mic...

Deep convolutional models improve predictions of macaque V1 responses to natural images.

PLoS computational biology
Despite great efforts over several decades, our best models of primary visual cortex (V1) still predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited understanding of the nonlinear computations in V1. Recent...

Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis.

IEEE journal of biomedical and health informatics
Image synthesis is a novel solution in precision medicine for scenarios where important medical imaging is not otherwise available. The convolutional neural network (CNN) is an ideal model for this task because of its powerful learning capabilities t...