AIMC Topic: Reproducibility of Results

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Exploring the use of machine learning for the assessment of skeletal fracture morphology and differentiation between impact mechanisms: A pilot study.

Journal of forensic sciences
Analyzing and interpreting traumatic injuries is a fundamental aspect of routine forensic case work. As the human skeleton can be impacted through a combination of loading mechanisms and varying impact energies, the analysis and interpretation of ske...

Application of Deep Learning in Civil Engineering Management.

Computational intelligence and neuroscience
Construction safety issues are of great significance in civil engineering management. In this paper, the entry point is the recognition of workers wearing helmets during the construction process, and the recognition performance is improved by combini...

Validation of a deep learning-based image analysis system to diagnose subclinical endometritis in dairy cows.

PloS one
The assessment of polymorphonuclear leukocyte (PMN) proportions (%) of endometrial samples is the hallmark for subclinical endometritis (SCE) diagnosis. Yet, a non-biased, automated diagnostic method for assessing PMN% in endometrial cytology slides ...

Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Hand gesture recognition with surface electromyography (sEMG) is indispensable for Muscle-Gesture-Computer Interface. The usual focus of it is upon performance evaluation involving the accuracy and robustness of hand gesture recognition. However, add...

Knowledge Graph Based Hard Drive Failure Prediction.

Sensors (Basel, Switzerland)
The hard drive is one of the important components of a computing system, and its failure can lead to both system failure and data loss. Therefore, the reliability of a hard drive is very important. Realising this importance, a number of studies have ...

Automatic deep learning multicontrast corpus callosum segmentation in multiple sclerosis.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: Corpus callosum (CC) atrophy is predictive of future disability in multiple sclerosis (MS). However, current segmentation methods are either labor- or computationally intensive. We therefore developed an automated deep learnin...

Testing Precision Limits of Neural Network-Based Quality Control Metrics in High-Throughput Digital Microscopy.

Pharmaceutical research
OBJECTIVE: Digital microscopy is used to monitor particulates such as protein aggregates within biopharmaceutical products. The images that result encode a wealth of information that is underutilized in pharmaceutical process monitoring. For example,...

A self-supervised domain-general learning framework for human ventral stream representation.

Nature communications
Anterior regions of the ventral visual stream encode substantial information about object categories. Are top-down category-level forces critical for arriving at this representation, or can this representation be formed purely through domain-general ...

Real-time image-based air quality estimation by deep learning neural networks.

Journal of environmental management
Air quality profoundly impacts public health and environmental equity. Efficient and inexpensive air quality monitoring instruments could be greatly beneficial for human health and air pollution control. This study proposes an image-based deep learni...

An Unsupervised Deep Learning Approach for Dynamic-Exponential Intravoxel Incoherent Motion MRI Modeling and Parameter Estimation in the Liver.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Dynamic-exponential intravoxel incoherent motion (IVIM) imaging is a potential technique for prediction, monitoring, and differential diagnosis of hepatic diseases, especially liver tumors. However, the use of such technique at voxel leve...