AIMC Topic: Algorithms

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Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists.

Journal of the National Cancer Institute
BACKGROUND: Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an ...

Efficient implementation of convolutional neural networks in the data processing of two-photon in vivo imaging.

Bioinformatics (Oxford, England)
MOTIVATION: Functional imaging at single-neuron resolution offers a highly efficient tool for studying the functional connectomics in the brain. However, mainstream neuron-detection methods focus on either the morphologies or activities of neurons, w...

An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening.

Journal of the National Cancer Institute
BACKGROUND: Human papillomavirus vaccination and cervical screening are lacking in most lower resource settings, where approximately 80% of more than 500 000 cancer cases occur annually. Visual inspection of the cervix following acetic acid applicati...

Predicting Inpatient Length of Stay After Brain Tumor Surgery: Developing Machine Learning Ensembles to Improve Predictive Performance.

Neurosurgery
BACKGROUND: Current outcomes prediction tools are largely based on and limited by regression methods. Utilization of machine learning (ML) methods that can handle multiple diverse inputs could strengthen predictive abilities and improve patient outco...

[A TrAdaBoost-based method for detecting multiple subjects' P300 potentials].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Individual differences of P300 potentials lead to that a large amount of training data must be collected to construct pattern recognition models in P300-based brain-computer interface system, which may cause subjects' fatigue and degrade the system p...

An Approach of Integrating Domain Knowledge into Data-Driven Diagnostic Model.

Studies in health technology and informatics
A diagnostic model of general diseases could help general practitioners to decrease misdiagnoses and reduce workload. In this paper, we developed a neural network model that can classify potential diagnoses among 100 selected common diseases based on...

Prediction of Clinical Events in Hemodialysis Patients Using an Artificial Neural Network.

Studies in health technology and informatics
Advanced chronic kidney disease (CKD) requires routine renal replacement therapy (RRT) that involves hemodialysis (HD) which may cause increased risk of muscle spasms, cardiovascular events, and death. We used Artificial Neural Network (ANN) method t...

Skin Lesion Detection with Support Vector Machines on iOS Devices.

Studies in health technology and informatics
Automated wound detection has become a common issue in health care. A broad variety of image processing algorithms already exist, but they are very power consuming on mobile devices. Meanwhile the use of machine learning algorithms is on the rise and...

Extracting Alcohol and Substance Abuse Status from Clinical Notes: The Added Value of Nursing Data.

Studies in health technology and informatics
We applied an open source natural language processing (NLP) system "NimbleMiner" to identify clinical notes with mentions of alcohol and substance abuse. NimbleMiner allows users to rapidly discover clinical vocabularies (using word embedding model) ...