AIMC Topic: Machine Learning

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Multi-modal medical image classification using deep residual network and genetic algorithm.

PloS one
Artificial intelligence (AI) development across the health sector has recently been the most crucial. Early medical information, identification, diagnosis, classification, then analysis, along with viable remedies, are always beneficial developments....

Predictive Uncertainty Estimation for Camouflaged Object Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Uncertainty is inherent in machine learning methods, especially those for camouflaged object detection aiming to finely segment the objects concealed in background. The strong enquote center bias of the training dataset leads to models of poor genera...

A Scoping Review on the Use of Machine Learning in Return-to-Work Studies: Strengths and Weaknesses.

Journal of occupational rehabilitation
PURPOSE: Decisions to increase work participation must be informed and timely to improve return to work (RTW). The implementation of research into clinical practice relies on sophisticated yet practical approaches such as machine learning (ML). The o...

Virtual pretreatment patient-specific quality assurance of volumetric modulated arc therapy using deep learning.

Medical physics
BACKGROUND: Automatic patient-specific quality assurance (PSQA) is recently explored using artificial intelligence approaches, and several studies reported the development of machine learning models for predicting the gamma pass rate (GPR) index only...

Finding functional motifs in protein sequences with deep learning and natural language models.

Current opinion in structural biology
Recently, prediction of structural/functional motifs in protein sequences takes advantage of powerful machine learning based approaches. Protein encoding adopts protein language models overpassing standard procedures. Different combinations of machin...

Metric Learning in Histopathological Image Classification: Opening the Black Box.

Sensors (Basel, Switzerland)
The application of machine learning techniques to histopathology images enables advances in the field, providing valuable tools that can speed up and facilitate the diagnosis process. The classification of these images is a relevant aid for physician...

Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning.

Sensors (Basel, Switzerland)
Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics tha...

Gene-specific machine learning for pathogenicity prediction of rare BRCA1 and BRCA2 missense variants.

Scientific reports
Machine learning-based pathogenicity prediction helps interpret rare missense variants of BRCA1 and BRCA2, which are associated with hereditary cancers. Recent studies have shown that classifiers trained using variants of a specific gene or a set of ...

Algorithmic fairness in artificial intelligence for medicine and healthcare.

Nature biomedical engineering
In healthcare, the development and deployment of insufficiently fair systems of artificial intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models stratified across subpopulations have revealed inequalities in how pat...

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives.

Pain research & management
Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assess...