AIMC Topic: Humans

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Fast fully automatic heart fat segmentation in computed tomography datasets.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Heart diseases affect a large part of the world's population. Studies have shown that these diseases are related to cardiac fat. Various medical diagnostic aid systems are developed to reduce these diseases. In this context, this paper presents a new...

Exploiting the stimuli encoding scheme of evolving Spiking Neural Networks for stream learning.

Neural networks : the official journal of the International Neural Network Society
Stream data processing has lately gained momentum with the arrival of new Big Data scenarios and applications dealing with continuously produced information flows. Unfortunately, traditional machine learning algorithms are not prepared to tackle the ...

Precise Quantitative Analysis of Cell Targeting by Particle-Based Agents Using Imaging Flow Cytometry and Convolutional Neural Network.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Understanding the intricacies of particle-cell interactions is essential for many applications such as imaging, phototherapy, and drug/gene delivery, because it is the key to accurate control of the particle properties for the improvement of their th...

Developing algorithms to predict adult onset internalizing disorders: An ensemble learning approach.

Journal of psychiatric research
A growing literature is utilizing machine learning methods to develop psychopathology risk algorithms that can be used to inform preventive intervention. However, efforts to develop algorithms for internalizing disorder onset have been limited. The g...

The use of machine learning techniques in trauma-related disorders: a systematic review.

Journal of psychiatric research
Establishing the diagnosis of trauma-related disorders such as Acute Stress Disorder (ASD) and Posttraumatic Stress Disorder (PTSD) have always been a challenge in clinical practice and in academic research, due to clinical and biological heterogenei...

The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases.

European journal of nuclear medicine and molecular imaging
PURPOSE: Although most deep learning (DL) studies have reported excellent classification accuracy, these studies usually target typical Alzheimer's disease (AD) and normal cognition (NC) for which conventional visual assessment performs well. A clini...

Deep Learning for Automated Measurement of Hemorrhage and Perihematomal Edema in Supratentorial Intracerebral Hemorrhage.

Stroke
Background and Purpose- Volumes of hemorrhage and perihematomal edema (PHE) are well-established biomarkers of primary and secondary injury, respectively, in spontaneous intracerebral hemorrhage. An automated imaging pipeline capable of accurately an...

Integrated structural modeling and super-resolution imaging resolve GPCR oligomers.

Progress in molecular biology and translational science
Formation of G protein-coupled receptors (GPCRs) dimers and higher order oligomers represents a key mechanism in pleiotropic signaling, yet how individual protomers function within oligomers remains poorly understood. For the Class A/rhodopsin subfam...