AI Medical Compendium Topic:
Supervised Machine Learning

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Automatic detection and diagnosis of sacroiliitis in CT scans as incidental findings.

Medical image analysis
Early diagnosis of sacroiliitis may lead to preventive treatment which can significantly improve the patient's quality of life in the long run. Oftentimes, a CT scan of the lower back or abdomen is acquired for suspected back pain. However, since the...

ClearF: a supervised feature scoring method to find biomarkers using class-wise embedding and reconstruction.

BMC medical genomics
BACKGROUND: Feature selection or scoring methods for the detection of biomarkers are essential in bioinformatics. Various feature selection methods have been developed for the detection of biomarkers, and several studies have employed information-the...

Ensemble-based kernel learning for a class of data assimilation problems with imperfect forward simulators.

PloS one
Simulator imperfection, often known as model error, is ubiquitous in practical data assimilation problems. Despite the enormous efforts dedicated to addressing this problem, properly handling simulator imperfection in data assimilation remains to be ...

Using Deep Neural Networks to Reconstruct Non-uniformly Sampled NMR Spectra.

Journal of biomolecular NMR
Non-uniform and sparse sampling of multi-dimensional NMR spectra has over the last decade become an important tool to allow for fast acquisition of multi-dimensional NMR spectra with high resolution. The success of non-uniform sampling NMR hinge on b...

Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT.

Medical image analysis
Classification of benign-malignant lung nodules on chest CT is the most critical step in the early detection of lung cancer and prolongation of patient survival. Despite their success in image classification, deep convolutional neural networks (DCNNs...

Iterative processes: a review of semi-supervised machine learning in rehabilitation science.

Disability and rehabilitation. Assistive technology
To define semi-supervised machine learning (SSML) and explore current and potential applications of this analytic strategy in rehabilitation research. We conducted a scoping review using PubMed, GoogleScholar and Medline. Studies were included if th...

A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection.

IEEE transactions on medical imaging
Glaucoma is one of the leading causes of irreversible vision loss. Many approaches have recently been proposed for automatic glaucoma detection based on fundus images. However, none of the existing approaches can efficiently remove high redundancy in...

Automated Flow Cytometric MRD Assessment in Childhood Acute B- Lymphoblastic Leukemia Using Supervised Machine Learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Minimal residual disease (MRD) as measured by multiparameter flow cytometry (FCM) is an independent and strong prognostic factor in B-cell acute lymphoblastic leukemia (B-ALL). However, reliable flow cytometric detection of MRD strongly depends on op...

Automatic classification of free-text medical causes from death certificates for reactive mortality surveillance in France.

International journal of medical informatics
BACKGROUND: Mortality surveillance is of fundamental importance to public health surveillance. The real-time recording of death certificates, thanks to Electronic Death Registration System (EDRS), provides valuable data for reactive mortality surveil...

Supervised Learning and Mass Spectrometry Predicts the Fate of Nanomaterials.

ACS nano
The surface of nanoparticles changes immediately after intravenous injection because blood proteins adsorb on the surface. How this interface changes during circulation and its impact on nanoparticle distribution within the body is not understood. He...