AIMC Topic: Machine Learning

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Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies.

Cancer biomarkers : section A of Disease markers Feb 6, 2018
Prostate is a second leading causes of cancer deaths among men. Early detection of cancer can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and multiresolution of MRIs from prostate cancer require a proper diagnostic...
Support Vector Machine Bayes Theorem Prostatic Neoplasms Machine Learning Male Humans
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Research digest: progress in microvascular complications.

The lancet. Diabetes & endocrinology Feb 1, 2018
Prevalence Diabetic Nephropathies Severity of Illness Index Humans Machine Learning Diabetic Retinopathy Diabetic Angiopathies Diabetes Mellitus, Type 2 Risk Factors Disease Progression
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Convolutional Invasion and Expansion Networks for Tumor Growth Prediction.

IEEE transactions on medical imaging Feb 1, 2018
Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics. Such models can be personalized based on clinical measurements to build th...
Precision Medicine Female Male Neural Networks, Computer Machine Learning Multimodal Imaging Neoplasms Humans Pancreatic Neoplasms Image Processing, Computer-Assisted Models, Biological Neoplasm Invasiveness
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VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability.

IEEE transactions on biomedical circuits and systems Feb 1, 2018
Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (...
Seizures Signal Processing, Computer-Assisted Algorithms Wearable Electronic Devices Humans Electroencephalography Machine Learning
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An On-Chip Learning Neuromorphic Autoencoder With Current-Mode Transposable Memory Read and Virtual Lookup Table.

IEEE transactions on biomedical circuits and systems Feb 1, 2018
This paper presents an IC implementation of on-chip learning neuromorphic autoencoder unit in a form of rate-based spiking neural network. With a current-mode signaling scheme embedded in a 500 × 500 6b SRAM-based memory, the proposed architecture ac...
Humans Neural Networks, Computer Machine Learning Databases, Factual Image Processing, Computer-Assisted Facial Recognition
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Use of Fetal Magnetic Resonance Image Analysis and Machine Learning to Predict the Need for Postnatal Cerebrospinal Fluid Diversion in Fetal Ventriculomegaly.

JAMA pediatrics Feb 1, 2018
IMPORTANCE: Which children with fetal ventriculomegaly, or enlargement of the cerebral ventricles in utero, will develop hydrocephalus requiring treatment after birth is unclear.
Prenatal Diagnosis Hydrocephalus Image Interpretation, Computer-Assisted Pregnancy Female Humans Infant, Newborn Machine Learning Retrospective Studies Case-Control Studies Magnetic Resonance Imaging Predictive Value of Tests
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Machine Learning and the Prediction of Hydrocephalus: Can Quantitative Image Analysis Assist the Clinician?

JAMA pediatrics Feb 1, 2018
Pregnancy Female Prenatal Care Hydrocephalus Magnetic Resonance Spectroscopy Humans Machine Learning
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Machine Learning Models for Prediction of Reinjury After Penetrating Trauma.

JAMA surgery Feb 1, 2018
This study compares several machine learning models for prediction of reinjury after penetrating trauma.
Mental Disorders Databases, Factual Suicide Humans Machine Learning Forecasting Wounds, Penetrating Patient Readmission Self-Injurious Behavior United States Recurrence Models, Theoretical
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Structure-based prediction of protein- peptide binding regions using Random Forest.

Bioinformatics (Oxford, England) Feb 1, 2018
MOTIVATION: Protein-peptide interactions are one of the most important biological interactions and play crucial role in many diseases including cancer. Therefore, knowledge of these interactions provides invaluable insights into all cellular processe...
Sequence Analysis, Protein Humans Machine Learning Protein Tyrosine Phosphatase, Non-Receptor Type 4 Peptides Proteins Protein Binding Protein Domains Computational Biology
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As above, so below? Towards understanding inverse models in BCI.

Journal of neural engineering Feb 1, 2018
OBJECTIVE: In brain-computer interfaces (BCI), measurements of the user's brain activity are classified into commands for the computer. With EEG-based BCIs, the origins of the classified phenomena are often considered to be spatially localized in the...
Humans Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Machine Learning Algorithms Brain
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