AIMC Topic: Artificial Intelligence

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Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration.

Information processing in medical imaging : proceedings of the ... conference
This paper presents a dictionary learning-based method to segment the brain surface in post-surgical CT images of epilepsy patients following surgical implantation of electrodes. Using the electrodes identified in the post-implantation CT, surgeons r...

Prediction of Longitudinal Development of Infant Cortical Surface Shape Using a 4D Current-Based Learning Framework.

Information processing in medical imaging : proceedings of the ... conference
Understanding the early dynamics of the highly folded human cerebral cortex is still an actively evolving research field teeming with unanswered questions. Longitudinal neuroimaging analysis and modeling have become the new trend to advance research ...

Finding a Path for Segmentation Through Sequential Learning.

Information processing in medical imaging : proceedings of the ... conference
Sequential learning techniques, such as auto-context, that applies the output of an intermediate classifier as contextual features for its subsequent classifier has shown impressive performance for semantic segmentation. We show that these methods ca...

Bodypart Recognition Using Multi-stage Deep Learning.

Information processing in medical imaging : proceedings of the ... conference
Automatic medical image analysis systems often start from identifying the human body part contained in the image; Specifically, given a transversal slice, it is important to know which body part it comes from, namely "slice-based bodypart recognition...

Shape Classification Using Wasserstein Distance for Brain Morphometry Analysis.

Information processing in medical imaging : proceedings of the ... conference
Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optim...

Chat-Bots for People with Parkinson's Disease: Science Fiction or Reality?

Studies in health technology and informatics
People with Parkinson's disease are known to have difficulties in language and communication. This paper proposes the use of an artificial conversational agent, commonly known as a chat-bot that runs on a smart-phone device and performs two-way conve...

A Review of Computational Intelligence Methods for Eukaryotic Promoter Prediction.

Nucleosides, nucleotides & nucleic acids
In past decades, prediction of genes in DNA sequences has attracted the attention of many researchers but due to its complex structure it is extremely intricate to correctly locate its position. A large number of regulatory regions are present in DNA...

A personalized health-monitoring system for elderly by combining rules and case-based reasoning.

Studies in health technology and informatics
Health-monitoring system for elderly in home environment is a promising solution to provide efficient medical services that increasingly interest by the researchers within this area. It is often more challenging when the system is self-served and fun...

Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

Studies in health technology and informatics
Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Dia...

Optimizing artificial neural network models for metabolomics and systems biology: an example using HPLC retention index data.

Bioanalysis
BACKGROUND: Artificial Neural Networks (ANN) are extensively used to model 'omics' data. Different modeling methodologies and combinations of adjustable parameters influence model performance and complicate model optimization.