AIMC Topic: Humans

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Detecting cells in intravital video microscopy using a deep convolutional neural network.

Computers in biology and medicine
The analysis of leukocyte recruitment in intravital video microscopy (IVM) is essential to the understanding of inflammatory processes. However, because IVM images often present a large variety of visual characteristics, it is hard for an expert huma...

Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer.

Medical image analysis
We apply for the first-time interpretable deep learning methods simultaneously to the most common skin cancers (basal cell carcinoma, squamous cell carcinoma and intraepidermal carcinoma) in a histological setting. As these three cancer types constit...

Combined virtual reality and haptic robotics induce space and movement invariant sensorimotor adaptation.

Neuropsychologia
Prism adaptation is a method for studying visuomotor plasticity in healthy individuals, as well as for rehabilitating patients suffering spatial neglect. We developed a new set-up based on virtual-reality (VR) and haptic-robotics allowing us to induc...

Age verification using random forests on facial 3D landmarks.

Forensic science international
Three-dimensional facial images are becoming more and more widespread. As such images provide more information about facial morphology than 2D imagery, they show great promise for use in future forensic applications, including age estimation and veri...

Compressed sensing and deep learning reconstruction for women's pelvic MRI denoising: Utility for improving image quality and examination time in routine clinical practice.

European journal of radiology
PURPOSE: To demonstrate the utility of compressed sensing with parallel imaging (Compressed SPEEDER) and AiCE compared with that of conventional parallel imaging (SPEEDER) for shortening examination time and improving image quality of women's pelvic ...

Toward reliable automatic liver and tumor segmentation using convolutional neural network based on 2.5D models.

International journal of computer assisted radiology and surgery
PURPOSE: We investigated the parameter configuration in the automatic liver and tumor segmentation using a convolutional neural network based on 2.5D model. The implementation of 2.5D model shows promising results since it allows the network to have ...

Artificial intelligence in transfusion medicine and its impact on the quality concept.

Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis

Emerging use of machine learning and advanced technologies to assess red cell quality.

Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis
Improving blood product quality and patient outcomes is an accepted goal in transfusion medicine research. Thus, there is an urgent need to understand the potential adverse effects on red blood cells (RBCs) during pre-transfusion storage. Current ass...

Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks.

Sensors (Basel, Switzerland)
In recent years, growing interest in deep learning neural networks has raised a question on how they can be used for effective processing of high-dimensional datasets produced by hyperspectral imaging (HSI). HSI, traditionally viewed as being within ...

Evaluation of the feasibility of an error-minimized approach to powered wheelchair skills training using shared control.

Disability and rehabilitation. Assistive technology
BACKGROUND: Powered wheelchairs promote participation for people with mobility limitations. For older adults with cognitive impairment, existing training methods may not address learning needs, leading to difficulty with powered wheelchair skills. Er...