AIMC Topic: Brain

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Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Semi-automatic measurements are performed on FDG PET-CT images to monitor the evolution of metastatic sites in the clinical follow-up of metastatic breast cancer patients. Apart from being time-consuming and prone to subjective approximation, semi-au...

Deep Learning for Neuroimaging Segmentation with a Novel Data Augmentation Strategy.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Brain insults such as cerebral ischemia and intracranial hemorrhage are critical stroke conditions with high mortality rates. Currently, medical image analysis for critical stroke conditions is still largely done manually, which is time-consuming and...

Anxiety and Depression Diagnosis Method Based on Brain Networks and Convolutional Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
At present, only professional doctors can use the professional scales to diagnose depression and anxiety in clinical practice. In recent years, the problems of detecting the presence of anxiety or depression using Electroencephalography (EEG) has rec...

Segmentation of Tau Stained Alzheimers Brain Tissue Using Convolutional Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Alzheimers disease is characterized by complex changes in brain tissue including the accumulation of tau-containing neurofibrillary tangles (NFTs) and dystrophic neurites (DNs) within neurons. The distribution and density of tau pathology throughout ...

A Two Cascaded Network Integrating Regional-based YOLO and 3D-CNN for Cerebral Microbleeds Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. In this paper, we propose a...

Depression Scale Prediction with Cross-Sample Entropy and Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
A two-stage deep learning-based scheme is presented to predict the Hamilton Depression Scale (HAM-D) in this study. First, the cross-sample entropy (CSE) that allows assessing the degree of similarity of two data series are evaluated for the 90 brain...

Automatic Identification of Brain Independent Components in Electroencephalography Data Collected while Standing in a Virtually Immersive Environment - A Deep Learning-Based Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Electroencephalography (EEG) is a commonly used method for monitoring brain activity. Automating an EEG signal processing pipeline is imperative to the exploration of real-time brain computer interface (BCI) applications. EEG analysis demands substan...

Frequency-dependent response in cortical network with periodic electrical stimulation.

Chaos (Woodbury, N.Y.)
Electrical stimulation can shape oscillations in brain activity. However, the mechanism of how periodic electrical stimulation modulates brain oscillations by time-delayed neural networks is poorly understood at present. To address this question, we ...

Automated human mind reading using EEG signals for seizure detection.

Journal of medical engineering & technology
Epilepsy is one of the most occurring neurological disease globally emerged back in 4000 BC. It is affecting around 50 million people of all ages these days. The trait of this disease is recurrent seizures. In the past few decades, the treatments ava...