AIMC Topic: Humans

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Visual Analytics of a Computer-Aided Diagnosis System for Pancreatic Lesions.

IEEE transactions on visualization and computer graphics
Machine learning is a powerful and effective tool for medical image analysis to perform computer-aided diagnosis (CAD). Having great potential in improving the accuracy of a diagnosis, CAD systems are often analyzed in terms of the final accuracy, le...

Evaluation for Parkinsonian Bradykinesia by deep learning modeling of kinematic parameters.

Journal of neural transmission (Vienna, Austria : 1996)
A wearable sensor system is available for monitoring of bradykinesia in patients with Parkinson's disease (PD), however, it remains unclear whether kinematic parameters would reflect clinical severity of PD, or would help clinical diagnosis of physic...

[Artificial intelligence in breast imaging : Areas of application from a clinical perspective].

Der Radiologe
CLINICAL/METHODOLOGICAL ISSUE: Central to breast imaging is the coordination of clinical and multimodal imaging information with percutaneous image-guided biopsies and surgical procedures. A wide range of problems arise due to this complexity: missed...

Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement.

Pacing and clinical electrophysiology : PACE
BACKGROUND: An accurate assessment of permanent pacemaker implantation (PPI) risk following transcatheter aortic valve replacement (TAVR) is important for clinical decision making. The aims of this study were to investigate the significance and utili...

HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks.

IEEE transactions on visualization and computer graphics
To mitigate the pain of manually tuning hyperparameters of deep neural networks, automated machine learning (AutoML) methods have been developed to search for an optimal set of hyperparameters in large combinatorial search spaces. However, the search...

VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data.

IEEE transactions on visualization and computer graphics
The fundamental motivation of the proposed work is to present a new visualization-guided computing paradigm to combine direct 3D volume processing and volume rendered clues for effective 3D exploration. For example, extracting and visualizing microst...

Detecting neurodevelopmental trajectories in congenital heart diseases with a machine-learning approach.

Scientific reports
We aimed to delineate the neuropsychological and psychopathological profiles of children with congenital heart disease (CHD) and look for associations with clinical parameters. We conducted a prospective observational study in children with CHD who u...

An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box.

Scientific reports
Falls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replace...

CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases.

BMC medical informatics and decision making
BACKGROUND: Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and pati...

Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network.

BMC medical imaging
BACKGROUND: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although...