AI Medical Compendium Topic

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Supervised Machine Learning

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Semi-supervised non-negative matrix factorization with structure preserving for image clustering.

Neural networks : the official journal of the International Neural Network Society
Semi-supervised learning methods have wide applications thanks to the reasonable utilization for a part of label information of data. In recent years, non-negative matrix factorization (NMF) has received considerable attention because of its interpre...

UniSAL: Unified Semi-supervised Active Learning for histopathological image classification.

Medical image analysis
Histopathological image classification using deep learning is crucial for accurate and efficient cancer diagnosis. However, annotating a large amount of histopathological images for training is costly and time-consuming, leading to a scarcity of avai...

Predictive reward-prediction errors of climbing fiber inputs integrate modular reinforcement learning with supervised learning.

PLoS computational biology
Although the cerebellum is typically associated with supervised learning algorithms, it also exhibits extensive involvement in reward processing. In this study, we investigated the cerebellum's role in executing reinforcement learning algorithms, wit...

A novel data-driven approach for Personas validation in healthcare using self-supervised machine learning.

Journal of biomedical informatics
OBJECTIVE: Persona validation is a challenging task, often relying on costly external validation methods. The aim of this study was the development of a novel method for Personas validation based on data already available during their creation.

Self-training EEG discrimination model with weakly supervised sample construction: An age-based perspective on ASD evaluation.

Neural networks : the official journal of the International Neural Network Society
Deep learning for Electroencephalography (EEG) has become dominant in the tasks of discrimination and evaluation of brain disorders. However, despite its significant successes, this approach has long been facing challenges due to the limited availabi...

Towards zero-shot human-object interaction detection via vision-language integration.

Neural networks : the official journal of the International Neural Network Society
Human-object interaction (HOI) detection aims to locate human-object pairs and identify their interaction categories in images. Most existing methods primarily focus on supervised learning, which relies on extensive manual HOI annotations. Such heavy...

Optimizing sample size for supervised machine learning with bulk transcriptomic sequencing: a learning curve approach.

Briefings in bioinformatics
Accurate sample classification using transcriptomics data is crucial for advancing personalized medicine. Achieving this goal necessitates determining a suitable sample size that ensures adequate classification accuracy without undue resource allocat...

A semi-supervised learning approach to classify drug attributes in a pharmacy management database: A STROBE-compliant study.

Medicine
With the development of information and communication technology, it has become possible to improve pharmacy management system (PMS) using these technologies. Our study aims to enhance the accuracy of drug attribute classification and recommend appro...

Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.

Microbial genomics
Diabetes mellitus is a complex metabolic disorder and one of the fastest-growing global public health concerns. The gut microbiota is implicated in the pathophysiology of various diseases, including diabetes. This study utilized 16S rRNA metagenomic ...

A semi-supervised prototypical network for prostate lesion segmentation from multimodality MRI.

Physics in medicine and biology
Prostate lesion segmentation from multiparametric magnetic resonance images is particularly challenging due to the limited availability of labeled data. This scarcity of annotated images makes it difficult for supervised models to learn the complex f...