AIMC Topic: Humans

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Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders.

Sensors (Basel, Switzerland)
In this research, we present a semi-supervised segmentation solution using convolutional autoencoders to solve the problem of segmentation tasks having a small number of ground-truth images. We evaluate the proposed deep network architecture for the ...

Applications of machine learning for simulations of red blood cells in microfluidic devices.

BMC bioinformatics
BACKGROUND: For optimization of microfluidic devices for the analysis of blood samples, it is useful to simulate blood cells as elastic objects in flow of blood plasma. In such numerical models, we primarily need to take into consideration the moveme...

A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis.

BMC bioinformatics
BACKGROUND: Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed...

DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images.

BMC bioinformatics
BACKGROUND: Melanoma results in the vast majority of skin cancer deaths during the last decades, even though this disease accounts for only one percent of all skin cancers' instances. The survival rates of melanoma from early to terminal stages is mo...

Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data.

BMC bioinformatics
BACKGROUND: Finding the tumor location in the prostate is an essential pathological step for prostate cancer diagnosis and treatment. The location of the tumor - the laterality - can be unilateral (the tumor is affecting one side of the prostate), or...

Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer.

Scientific reports
A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling training models on multicenter data without data lea...

Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry.

Physics in medicine and biology
Radio-frequency dosimetry is an important process in assessments for human exposure safety and for compliance of related products. Recently, computational human models generated from medical images have often been used for such assessment, especially...

Machine Learning in Drug Discovery and Development Part 1: A Primer.

CPT: pharmacometrics & systems pharmacology
Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and develop...

Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography.

Computational intelligence and neuroscience
Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of computed tomography (CT) or magnetic resonance imaging (MRI) is required to determine the severity of...

Early short-term prediction of emergency department length of stay using natural language processing for low-acuity outpatients.

The American journal of emergency medicine
BACKGROUND: Low-acuity outpatients constitute the majority of emergency department (ED) patients, and these patients often experience an unpredictable length of stay (LOS). Effective LOS prediction might improve the quality of ED care and reduce ED c...