AIMC Topic: Humans

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A deep learning model for real-time mortality prediction in critically ill children.

Critical care (London, England)
BACKGROUND: The rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine l...

Fully automatic catheter segmentation in MRI with 3D convolutional neural networks: application to MRI-guided gynecologic brachytherapy.

Physics in medicine and biology
External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-tissue contrast provided by magnetic resonance imaging (MRI) makes it a valuable imaging modality fo...

Deep learning-based electroencephalography analysis: a systematic review.

Journal of neural engineering
CONTEXT: Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great ...

Dynamic network modeling and dimensionality reduction for human ECoG activity.

Journal of neural engineering
OBJECTIVE: Developing dynamic network models for multisite electrocorticogram (ECoG) activity can help study neural representations and design neurotechnologies in humans given the clinical promise of ECoG. However, dynamic network models have so far...

Siamese Neural Networks for User Identity Linkage Through Web Browsing.

IEEE transactions on neural networks and learning systems
Linking online identities of users among countless heterogeneous network services on the Internet can provide an explicit digital representation of users, which can benefit both research and industry. In recent years, user identity linkage (UIL) thro...

Label-less Learning for Emotion Cognition.

IEEE transactions on neural networks and learning systems
In this paper, we propose a label-less learning for emotion cognition (LLEC) to achieve the utilization of a large amount of unlabeled data. We first inspect the unlabeled data from two perspectives, i.e., the feature layer and the decision layer. By...

Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation.

IEEE transactions on medical imaging
Accurate segmentation of the prostate from magnetic resonance (MR) images provides useful information for prostate cancer diagnosis and treatment. However, automated prostate segmentation from 3D MR images faces several challenges. The lack of clear ...

Exploring Skills Gained Through a Robotics Program for Youth With Disabilities.

OTJR : occupation, participation and health
Children with disabilities often have fewer opportunities to engage in science, technology, engineering, and math programs that can enhance their educational and career opportunities. This study explored the quality, experience, and skills learned in...