AIMC Topic: Deep Learning

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Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning.

Computer methods and programs in biomedicine
INTRODUCTION: With biomedical imaging research increasingly using large datasets, it becomes critical to find operator-free methods to quality control the data collected and the associated analysis. Attempts to use artificial intelligence (AI) to per...

Technical Note: Dose prediction for head and neck radiotherapy using a three-dimensional dense dilated U-net architecture.

Medical physics
PURPOSE: Radiation therapy treatment planning is a time-consuming and iterative manual process. Consequently, plan quality varies greatly between and within institutions. Artificial intelligence shows great promise in improving plan quality and reduc...

Technical Note: Dose prediction for radiation therapy using feature-based losses and One Cycle Learning.

Medical physics
PURPOSE: To present the technical details of the runner-up model in the open knowledge-based planning (OpenKBP) challenge for the dose-volume histogram (DVH) stream. The model was designed to ensure simple and reproducible training, without the neces...

Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection.

Computers in biology and medicine
The COVID-19 outbreak has resulted in a global pandemic and led to more than a million deaths to date. COVID-19 early detection is essential for its mitigation by controlling its spread from infected patients in communities through quarantine. Althou...

COVID-19 deep classification network based on convolution and deconvolution local enhancement.

Computers in biology and medicine
Computer Tomography (CT) detection can effectively overcome the problems of traditional detection of Corona Virus Disease 2019 (COVID-19), such as lagging detection results and wrong diagnosis results, which lead to the increase of disease infection ...

On-Site Detection of SARS-CoV-2 Antigen by Deep Learning-Based Surface-Enhanced Raman Spectroscopy and Its Biochemical Foundations.

Analytical chemistry
A rapid, on-site, and accurate SARS-CoV-2 detection method is crucial for the prevention and control of the COVID-19 epidemic. However, such an ideal screening technology has not yet been developed for the diagnosis of SARS-CoV-2. Here, we have devel...

MEG Source Localization via Deep Learning.

Sensors (Basel, Switzerland)
We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned to single and multiple time point MEG data, and can estimate varying numbers of dipole s...

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals.

Sensors (Basel, Switzerland)
The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and COVID-19 has also led to a contactles...

Detecting neonatal acute bilirubin encephalopathy based on T1-weighted MRI images and learning-based approaches.

BMC medical imaging
BACKGROUND: Neonatal hyperbilirubinemia is a common clinical condition that requires medical attention in newborns, which may develop into acute bilirubin encephalopathy with a significant risk of long-term neurological deficits. The current clinical...

Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations.

Cell reports methods
Structure prediction for proteins lacking homologous templates in the Protein Data Bank (PDB) remains a significant unsolved problem. We developed a protocol, C-I-TASSER, to integrate interresidue contact maps from deep neural-network learning with t...