AIMC Topic: Machine Learning

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Fiber Bundle Image Reconstruction Using Convolutional Neural Networks and Bundle Rotation in Endomicroscopy.

Sensors (Basel, Switzerland)
Fiber-bundle endomicroscopy has several recognized drawbacks, the most prominent being the honeycomb effect. We developed a multi-frame super-resolution algorithm exploiting bundle rotation to extract features and reconstruct underlying tissue. Simul...

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations.

PloS one
Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models is difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. W...

Predicting mechanical properties of silk from its amino acid sequences via machine learning.

Journal of the mechanical behavior of biomedical materials
The silk fiber is increasingly being sought for its superior mechanical properties, biocompatibility, and eco-friendliness, making it promising as a base material for various applications. One of the characteristics of protein fibers, such as silk, i...

Modeling Production-Living-Ecological Space for Chengdu, China: An Analytical Framework Based on Machine Learning with Automatic Parameterization of Environmental Elements.

International journal of environmental research and public health
Cities worldwide are facing the dual pressures of growing population and land expansion, leading to the intensification of conflicts in urban productive-living-ecological spaces (PLES). Therefore, the question of "how to dynamically judge the differe...

Uncovering the complex genetic architecture of human plasma lipidome using machine learning methods.

Scientific reports
Genetic architecture of plasma lipidome provides insights into regulation of lipid metabolism and related diseases. We applied an unsupervised machine learning method, PGMRA, to discover phenotype-genotype many-to-many relations between genotype and ...

Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.

Investigative radiology
With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce...

Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark.

Medical image analysis
PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robot...

Artificial intelligence based personalized predictive survival among colorectal cancer patients.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Colorectal cancer is a major health concern. It is now the third most common cancer and the fourth leading cause of cancer mortality worldwide. The aim of this study was to evaluate the performance of machine learning algori...

A Machine Learning-Combined Flexible Sensor for Tactile Detection and Voice Recognition.

ACS applied materials & interfaces
Intelligent sensors have attracted substantial attention for various applications, including wearable electronics, artificial intelligence, healthcare monitoring, and human-machine interactions. However, there still remains a critical challenge in de...

Machine learning approaches to predict drug efficacy and toxicity in oncology.

Cell reports methods
In recent years, there has been a surge of interest in using machine learning algorithms (MLAs) in oncology, particularly for biomedical applications such as drug discovery, drug repurposing, diagnostics, clinical trial design, and pharmaceutical pro...