AI Medical Compendium Topic:
Supervised Machine Learning

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Supervised and semi-supervised probabilistic learning with deep neural networks for concurrent process-quality monitoring.

Neural networks : the official journal of the International Neural Network Society
Concurrent process-quality monitoring helps discover quality-relevant process anomalies and quality-irrelevant process anomalies. It especially works well in chemical plants with faults that cause quality problems. Traditional monitoring strategies a...

Semi-supervised task-driven data augmentation for medical image segmentation.

Medical image analysis
Supervised learning-based segmentation methods typically require a large number of annotated training data to generalize well at test time. In medical applications, curating such datasets is not a favourable option because acquiring a large number of...

New Approach for Risk Estimation Algorithms of Negativeness Detection with Modelling Supervised Machine Learning Techniques.

Disease markers
gene testing is a difficult, expensive, and time-consuming test which requires excessive work load. The identification of the gene mutations is significantly important in the selection of treatment and the risk of secondary cancer. We aimed to deve...

Towards Safe Weakly Supervised Learning.

IEEE transactions on pattern analysis and machine intelligence
In this paper, we study weakly supervised learning where a large amount of data supervision is not accessible. This includes i) incomplete supervision, where only a small subset of labels is given, such as semi-supervised learning and domain adaptati...

MS-CAM: Multi-Scale Class Activation Maps for Weakly-Supervised Segmentation of Geographic Atrophy Lesions in SD-OCT Images.

IEEE journal of biomedical and health informatics
As one of the most critical characteristics in advanced stage of non-exudative Age-related Macular Degeneration (AMD), Geographic Atrophy (GA) is one of the significant causes of sustained visual acuity loss. Automatic localization of retinal regions...

UD-MIL: Uncertainty-Driven Deep Multiple Instance Learning for OCT Image Classification.

IEEE journal of biomedical and health informatics
Deep learning has achieved remarkable success in the optical coherence tomography (OCT) image classification task with substantial labelled B-scan images available. However, obtaining such fine-grained expert annotations is usually quite difficult an...

Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods.

IEEE transactions on medical imaging
Medical imaging systems are commonly assessed and optimized by use of objective measures of image quality (IQ). The Ideal Observer (IO) performance has been advocated to provide a figure-of-merit for use in assessing and optimizing imaging systems be...

Distant Supervision Relation Extraction via adaptive dependency-path and additional knowledge graph supervision.

Neural networks : the official journal of the International Neural Network Society
Relation Extraction systems train an extractor by aligning relation instances in Knowledge Base with a large amount of labeled corpora. Since the labeled datasets are very expensive, Distant Supervision Relation Extraction (DSRE) utilizes rough corpu...

Comparing supervised and unsupervised approaches to emotion categorization in the human brain, body, and subjective experience.

Scientific reports
Machine learning methods provide powerful tools to map physical measurements to scientific categories. But are such methods suitable for discovering the ground truth about psychological categories? We use the science of emotion as a test case to expl...

Using machine learning-based analytics of daily activities to identify modifiable risk factors for falling in Parkinson's disease.

Parkinsonism & related disorders
BACKGROUND: Although risk factors that lead to falling in Parkinson's disease (PD) have been previously studied, the established predictors are mostly non-modifiable. A novel method for fall risk assessment may provide more insight into preventable h...