AIMC Topic: Machine Learning

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Improving Arterial Spin Labeling by Using Deep Learning.

Radiology
Purpose To develop a deep learning algorithm that generates arterial spin labeling (ASL) perfusion images with higher accuracy and robustness by using a smaller number of subtraction images. Materials and Methods For ASL image generation from pair-wi...

MRI radiomics analysis of molecular alterations in low-grade gliomas.

International journal of computer assisted radiology and surgery
PURPOSE: Low-grade gliomas (LGG) are classified into three distinct groups based on their IDH1 mutation and 1p/19q codeletion status, each of which is associated with a different clinical expression. The genomic sub-classification of LGG requires tum...

Classifying cancer genome aberrations by their mutually exclusive effects on transcription.

BMC medical genomics
BACKGROUND: Malignant tumors are typically caused by a conglomeration of genomic aberrations-including point mutations, small insertions, small deletions, and large copy-number variations. In some cases, specific chemotherapies and targeted drug trea...

Prediction of Hemorrhagic Transformation Severity in Acute Stroke From Source Perfusion MRI.

IEEE transactions on bio-medical engineering
OBJECTIVE: Hemorrhagic transformation (HT) is the most severe complication of reperfusion therapy in acute ischemic stroke (AIS) patients. Management of AIS patients could benefit from accurate prediction of upcoming HT. While prediction of HT occurr...

Big Data in Public Health: Terminology, Machine Learning, and Privacy.

Annual review of public health
The digital world is generating data at a staggering and still increasing rate. While these "big data" have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores se...

Epileptic seizure detection in EEG signal using machine learning techniques.

Australasian physical & engineering sciences in medicine
Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are ti...

Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation.

Medical image analysis
Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially importa...

Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks.

Cell systems
While genes are defined by sequence, in biological systems a protein's function is largely determined by its three-dimensional structure. Evolutionary information embedded within multiple sequence alignments provides a rich source of data for inferri...

A multiple distributed representation method based on neural network for biomedical event extraction.

BMC medical informatics and decision making
BACKGROUND: Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification...