AI Medical Compendium Topic

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Deep Modular Bilinear Attention Network for Visual Question Answering.

Sensors (Basel, Switzerland)
VQA (Visual Question Answering) is a multi-model task. Given a picture and a question related to the image, it will determine the correct answer. The attention mechanism has become a de facto component of almost all VQA models. Most recent VQA approa...

A New Method of Deep Convolutional Neural Network Image Classification Based on Knowledge Transfer in Small Label Sample Environment.

Sensors (Basel, Switzerland)
The problem of deep learning network image classification when a large number of image samples are obtained in life and with only a small amount of knowledge annotation, is preliminarily solved in this paper. First, a support vector machine expert la...

A Real-Time Zanthoxylum Target Detection Method for an Intelligent Picking Robot under a Complex Background, Based on an Improved YOLOv5s Architecture.

Sensors (Basel, Switzerland)
The target recognition algorithm is one of the core technologies of Zanthoxylum pepper-picking robots. However, most existing detection algorithms cannot effectively detect Zanthoxylum fruit covered by branches, leaves and other fruits in natural sce...

Understanding machine learning classifier decisions in automated radiotherapy quality assurance.

Physics in medicine and biology
The complexity of generating radiotherapy treatments demands a rigorous quality assurance (QA) process to ensure patient safety and to avoid clinically significant errors. Machine learning classifiers have been explored to augment the scope and effic...

Semi-supervised learning for medical image classification using imbalanced training data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Medical image classification is often challenging for two reasons: a lack of labelled examples due to expensive and time-consuming annotation protocols, and imbalanced class labels due to the relative scarcity of disease-pos...

Machine learning prediction model of acute kidney injury after percutaneous coronary intervention.

Scientific reports
Acute kidney injury (AKI) after percutaneous coronary intervention (PCI) is associated with a significant risk of morbidity and mortality. The traditional risk model provided by the National Cardiovascular Data Registry (NCDR) is useful for predictin...

Imputation by feature importance (IBFI): A methodology to envelop machine learning method for imputing missing patterns in time series data.

PloS one
A new methodology, imputation by feature importance (IBFI), is studied that can be applied to any machine learning method to efficiently fill in any missing or irregularly sampled data. It applies to data missing completely at random (MCAR), missing ...

Classifying microscopic images as acute lymphoblastic leukemia by Resnet ensemble model and Taguchi method.

BMC bioinformatics
BACKGROUND: Researchers have attempted to apply deep learning methods of artificial intelligence for rapidly and accurately detecting acute lymphoblastic leukemia (ALL) in microscopic images.

The use of machine learning to discover regulatory networks controlling biological systems.

Molecular cell
Biological systems are composed of a vast web of multiscale molecular interactors and interactions. High-throughput technologies, both bulk and single cell, now allow for investigation of the properties and quantities of these interactors. Computatio...

Parametric Neural Network-Based Model Free Adaptive Tracking Control Method and Its Application to AFS/DYC System.

Computational intelligence and neuroscience
This paper deals with adaptive nonlinear identification and trajectory tracking problem for model free nonlinear systems via parametric neural network (PNN). Firstly, a more effective PNN identifier is developed to obtain the unknown system dynamics,...