AIMC Topic: Data Accuracy

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High-content image generation for drug discovery using generative adversarial networks.

Neural networks : the official journal of the International Neural Network Society
Immense amount of high-content image data generated in drug discovery screening requires computationally driven automated analysis. Emergence of advanced machine learning algorithms, like deep learning models, has transformed the interpretation and a...

Review of medical image recognition technologies to detect melanomas using neural networks.

BMC bioinformatics
BACKGROUND: Melanoma is one of the most aggressive types of cancer that has become a world-class problem. According to the World Health Organization estimates, 132,000 cases of the disease and 66,000 deaths from malignant melanoma and other forms of ...

Review of Clinical Research Informatics.

Yearbook of medical informatics
OBJECTIVES: Clinical Research Informatics (CRI) declares its scope in its name, but its content, both in terms of the clinical research it supports-and sometimes initiates-and the methods it has developed over time, reach much further than the name s...

Improving the accuracy of medical diagnosis with causal machine learning.

Nature communications
Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient's symptoms by determining the diseases causing them. However, existing machine learning approaches to diagnosis...

Deep learning-based classification of the mouse estrous cycle stages.

Scientific reports
There is a rapidly growing demand for female animals in preclinical animal, and thus it is necessary to determine animals' estrous cycle stages from vaginal smear cytology. However, the determination of estrous stages requires extensive training, tak...

Exploiting defective RRAM array as synapses of HTM spatial pooler with boost-factor adjustment scheme for defect-tolerant neuromorphic systems.

Scientific reports
A crossbar array architecture employing resistive switching memory (RRAM) as a synaptic element accelerates vector-matrix multiplication in a parallel fashion, enabling energy-efficient pattern recognition. To implement the function of the synapse in...

Feature Selection for High-Dimensional and Imbalanced Biomedical Data Based on Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm.

Genes
The training machine learning algorithm from an imbalanced data set is an inherently challenging task. It becomes more demanding with limited samples but with a massive number of features (high dimensionality). The high dimensional and imbalanced dat...

Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study.

The Lancet. Digital health
BACKGROUND: Preterm birth is a major global health challenge, the leading cause of death in children under 5 years of age, and a key measure of a population's general health and nutritional status. Current clinical methods of estimating fetal gestati...

Missing data imputation with adversarially-trained graph convolutional networks.

Neural networks : the official journal of the International Neural Network Society
Missing data imputation (MDI) is the task of replacing missing values in a dataset with alternative, predicted ones. Because of the widespread presence of missing data, it is a fundamental problem in many scientific disciplines. Popular methods for M...

Detecting lane change maneuvers using SHRP2 naturalistic driving data: A comparative study machine learning techniques.

Accident; analysis and prevention
Lane change has been recognized as a challenging driving maneuver and a significant component of traffic safety research. Developing a real-time continuous lane change detection system can assist drivers to perform and deal with complex driving tasks...