AIMC Topic: Deep Learning

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Spatio-temporal graph convolutional network for diagnosis and treatment response prediction of major depressive disorder from functional connectivity.

Human brain mapping
The pathophysiology of major depressive disorder (MDD) has been explored to be highly associated with the dysfunctional integration of brain networks. It is therefore imperative to explore neuroimaging biomarkers to aid diagnosis and treatment. In th...

Which GAN? A comparative study of generative adversarial network-based fast MRI reconstruction.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Fast magnetic resonance imaging (MRI) is crucial for clinical applications that can alleviate motion artefacts and increase patient throughput. -space undersampling is an obvious approach to accelerate MR acquisition. However, undersampling of -space...

Dark corner artefact and diagnostic performance of a market-approved neural network for skin cancer classification.

Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG
BACKGROUND AND OBJECTIVES: Convolutional neural networks (CNN) have proven dermatologist-level performance in skin lesion classification. Prior to a broader clinical application, an assessment of limitations is crucial. Therefore, the influence of a ...

Toward an Automatic Quality Assessment of Voice-Based Telemedicine Consultations: A Deep Learning Approach.

Sensors (Basel, Switzerland)
Maintaining a high quality of conversation between doctors and patients is essential in telehealth services, where efficient and competent communication is important to promote patient health. Assessing the quality of medical conversations is often h...

Generative adversarial network for glioblastoma ensures morphologic variations and improves diagnostic model for isocitrate dehydrogenase mutant type.

Scientific reports
Generative adversarial network (GAN) creates synthetic images to increase data quantity, but whether GAN ensures meaningful morphologic variations is still unknown. We investigated whether GAN-based synthetic images provide sufficient morphologic var...

Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time.

Scientific reports
Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using har...

Deep learning-based predictive identification of neural stem cell differentiation.

Nature communications
The differentiation of neural stem cells (NSCs) into neurons is proposed to be critical in devising potential cell-based therapeutic strategies for central nervous system (CNS) diseases, however, the determination and prediction of differentiation is...

AECG-DecompNet: abdominal ECG signal decomposition through deep-learning model.

Physiological measurement
The accurate decomposition of a mother's abdominal electrocardiogram (AECG) to extract the fetal ECG (FECG) is a primary step in evaluating the fetus's health. However, the AECG is often affected by different noises and interferences, such as the mat...

Predicting mean ribosome load for 5'UTR of any length using deep learning.

PLoS computational biology
The 5' untranslated region plays a key role in regulating mRNA translation and consequently protein abundance. Therefore, accurate modeling of 5'UTR regulatory sequences shall provide insights into translational control mechanisms and help interpret ...

Breast glandularity and mean glandular dose assessment using a deep learning framework: Virtual patients study.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Breast dosimetry in mammography is an important aspect of radioprotection since women are exposed periodically to ionizing radiation due to breast cancer screening programs. Mean glandular dose (MGD) is the standard quantity employed for the...