AIMC Topic: Reproducibility of Results

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Automatic Evaluation of Motor Rehabilitation Exercises Based on Deep Mixture Density Neural Networks.

Journal of biomedical informatics
An automatic assessment system for physical telerehabilitation could reduce the time and cost of treatments. But such assessment involves stochastic uncertainties, nonlinearities, and complexities of human movement. Probabilistic models and deep stru...

Vision Transformer for femur fracture classification.

Injury
INTRODUCTION: In recent years, the scientific community focused on developing Computer-Aided Diagnosis (CAD) tools that could improve clinicians' bone fractures diagnosis, primarily based on Convolutional Neural Networks (CNNs). However, the discerni...

A method to classify bone marrow cells with rejected option.

Biomedizinische Technik. Biomedical engineering
Bone marrow cell morphology has always been an important tool for the diagnosis of blood diseases. Still, it requires years of experience from a suitable person. Furthermore, the outcomes of their recognition are subjective and there is no objective ...

ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: As deep learning faces a reproducibility crisis and studies on deep learning applied to neuroimaging are contaminated by methodological flaws, there is an urgent need to provide a safe environment for deep learning users to ...

Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform.

BMC oral health
BACKGROUND: Artificial Intelligence has created a huge impact in different areas of dentistry. Automated cephalometric analysis is one of the major applications of artificial intelligence in the field of orthodontics. Various automated cephalometric ...

The stability of oncologic MRI radiomic features and the potential role of deep learning: a review.

Physics in medicine and biology
The use of MRI radiomic models for the diagnosis, prognosis and treatment response prediction of tumors has been increasingly reported in literature. However, its widespread adoption in clinics is hampered by issues related to features stability. In ...

Online Course Model of Social and Political Education Using Deep Learning.

Computational intelligence and neuroscience
This study aims to improve the social and political literacy of college students. Social and Political Education (SPE) is studied for undergraduates. Firstly, the background of the subject research is introduced. The face recognition module is built ...

MarkerML - Marker Feature Identification in Metagenomic Datasets Using Interpretable Machine Learning.

Journal of molecular biology
Identification of environment specific marker-features is one of the key objectives of many metagenomic studies. It aims to identify such features in microbiome datasets that may serve as markers of the contrasting or comparable states. Hypothesis te...

Bayesian deep learning outperforms clinical trial estimators of intracerebral and intraventricular hemorrhage volume.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: Intracerebral hemorrhage (ICH) and intraventricular hemorrhage (IVH) clinical trials rely on manual linear and semi-quantitative (LSQ) estimators like the ABC/2, modified Graeb and IVH scores for timely volumetric estimation f...

Natural language processing and String Metric-assisted Assessment of Semantic Heterogeneity method for capturing and standardizing unstructured nursing activities in a hospital setting: a retrospective study.

Annali di igiene : medicina preventiva e di comunita
BACKGROUND: Nurses record data in electronic health records (EHRs) using different terminologies and coding systems. The purpose of this study was to identify unstructured free-text nursing activities recorded by nurses in EHRs with natural language ...