AI Medical Compendium Topic:
Supervised Machine Learning

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Retinal blood vessel extraction employing effective image features and combination of supervised and unsupervised machine learning methods.

Artificial intelligence in medicine
In medicine, retinal vessel analysis of fundus images is a prominent task for the screening and diagnosis of various ophthalmological and cardiovascular diseases. In this research, a method is proposed for extracting the retinal blood vessels employi...

Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records.

BMC medical informatics and decision making
BACKGROUND: Life expectancy is one of the most important factors in end-of-life decision making. Good prognostication for example helps to determine the course of treatment and helps to anticipate the procurement of health care services and facilitie...

Exploring semi-supervised variational autoencoders for biomedical relation extraction.

Methods (San Diego, Calif.)
The biomedical literature provides a rich source of knowledge such as protein-protein interactions (PPIs), drug-drug interactions (DDIs) and chemical-protein interactions (CPIs). Biomedical relation extraction aims to automatically extract biomedical...

Pixel-to-Pixel Learning With Weak Supervision for Single-Stage Nucleus Recognition in Ki67 Images.

IEEE transactions on bio-medical engineering
OBJECTIVE: Nucleus recognition is a critical yet challenging step in histopathology image analysis, for example, in Ki67 immunohistochemistry stained images. Although many automated methods have been proposed, most use a multi-stage processing pipeli...

Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data.

IEEE transactions on neural networks and learning systems
Principal component analysis (PCA) has been used to study the pathogenesis of diseases. To enhance the interpretability of classical PCA, various improved PCA methods have been proposed to date. Among these, a typical method is the so-called sparse P...

Active learning using rough fuzzy classifier for cancer prediction from microarray gene expression data.

Journal of biomedical informatics
Cancer classification from microarray gene expression data is one of the important areas of research in the field of computational biology and bioinformatics. Traditional supervised techniques often fail to produce desired accuracy as the number of c...

Synthesizing Supervision for Learning Deep Saliency Network without Human Annotation.

IEEE transactions on pattern analysis and machine intelligence
Recently, the research field of salient object detection is undergoing a rapid and remarkable development along with the wide usage of deep neural networks. Being trained with a large number of images annotated with strong pixel-level ground-truth ma...

Low-rank network signatures in the triple network separate schizophrenia and major depressive disorder.

NeuroImage. Clinical
Brain imaging studies have revealed that functional and structural brain connectivity in the so-called triple network (i.e., default mode network (DMN), salience network (SN) and central executive network (CEN)) are consistently altered in schizophre...

Constrained-CNN losses for weakly supervised segmentation.

Medical image analysis
Weakly-supervised learning based on, e.g., partially labelled images or image-tags, is currently attracting significant attention in CNN segmentation as it can mitigate the need for full and laborious pixel/voxel annotations. Enforcing high-order (gl...

A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models.

Journal of clinical epidemiology
OBJECTIVES: The objective of this study was to compare performance of logistic regression (LR) with machine learning (ML) for clinical prediction modeling in the literature.