AIMC Topic: Reproducibility of Results

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The Impact of Cross-Validation Schemes for EEG-Based Auditory Attention Detection with Deep Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study assesses the performance of different cross-validation splits for brain-signal-based Auditory Attention Decoding (AAD) using deep neural networks on three publicly available Electroencephalography datasets. We investigate the effect of tri...

Feature Imitating Networks Enhance the Performance, Reliability and Speed of Deep Learning on Biomedical Image Processing Tasks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Feature-Imitating-Networks (FINs) are neural networks that are first trained to approximate closed-form statistical features (e.g. Entropy), and then embedded into other networks to enhance their performance. In this work, we perform the first evalua...

Smartphone-Based Balance Assessment Using Machine Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study explores the potential of smartphones to objectively assess balance, which is crucial for the elderly and individuals recovering from various medical conditions. We propose an innovative methodology to estimate the Modified Clinical Test o...

Deep Left Ventricular Motion Estimation Methods in Echocardiography: A Comparative Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Motion estimation in echocardiography is critical when assessing heart function and calculating myocardial deformation indices. Nevertheless, there are limitations in clinical practice, particularly with regard to the accuracy and reliability of meas...

Multi-Scale Self-Supervised Consistency Training for Trustworthy Medical Imaging Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Modern neural network models have demonstrated exceptional classification capabilities comparable to human performance in various medical diagnosis tasks. However, their practical application in real-world medical scenarios is hindered by an issue kn...

A nested cross validation approach to machine learning model performance evaluation on a small dataset for Creutzfeldt-Jakob disease diagnosis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The use of machine learning (ML) to diagnose neurological diseases has become increasingly popular. However, some rare neurodegenerative diseases such as Creutzfeldt-Jakob disease (CJD) suffer from the way that the traditional diagnosis relying abnor...

A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In practical scenarios involving the measurement of surface electromyography (sEMG) in muscles, particularly those areas near the heart, one of the primary sources of contamination is the presence of electrocardiogram (ECG) signals. To assess the qua...

Instance-Wise MRI Reconstruction Based on Self-Supervised Implicit Neural Representation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Accelerated MRI involves a trade-off between sampling sufficiency and acquisition time. Supervised deep learning methods have shown great success in MRI reconstruction from under-sampled measurements, but they typically require a large set of fully-s...

Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis: An observational study.

Medicine
The diagnosis of pneumoconiosis is complex and subjective, leading to inevitable variability in readings. This is especially true for inexperienced doctors. To improve accuracy, a computer-assisted diagnosis system is used for more effective pneumoco...

Acute myocardial infarction prognosis prediction with reliable and interpretable artificial intelligence system.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Predicting mortality after acute myocardial infarction (AMI) is crucial for timely prescription and treatment of AMI patients, but there are no appropriate AI systems for clinicians. Our primary goal is to develop a reliable and interpreta...