AIMC Topic: Deep Learning

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A deep learning model for detection and tracking in high-throughput images of organoid.

Computers in biology and medicine
Organoid, an in vitro 3D culture, has extremely high similarity with its source organ or tissue, which creates a model in vitro that simulates the in vivo environment. Organoids have been extensively studied in cell biology, precision medicine, drug ...

Deep cross-modality (MR-CT) educed distillation learning for cone beam CT lung tumor segmentation.

Medical physics
PURPOSE: Despite the widespread availability of in-treatment room cone beam computed tomography (CBCT) imaging, due to the lack of reliable segmentation methods, CBCT is only used for gross set up corrections in lung radiotherapies. Accurate and reli...

Mini-COVIDNet: Efficient Lightweight Deep Neural Network for Ultrasound Based Point-of-Care Detection of COVID-19.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Lung ultrasound (US) imaging has the potential to be an effective point-of-care test for detection of COVID-19, due to its ease of operation with minimal personal protection equipment along with easy disinfection. The current state-of-the-art deep le...

Variational Formulation of Unsupervised Deep Learning for Ultrasound Image Artifact Removal.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Recently, deep learning approaches have been successfully used for ultrasound (US) image artifact removal. However, paired high-quality images for supervised training are difficult to obtain in many practical situations. Inspired by the recent theory...

Protein model accuracy estimation empowered by deep learning and inter-residue distance prediction in CASP14.

Scientific reports
The inter-residue contact prediction and deep learning showed the promise to improve the estimation of protein model accuracy (EMA) in the 13th Critical Assessment of Protein Structure Prediction (CASP13). To further leverage the improved inter-resid...

Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning.

BMC medical informatics and decision making
BACKGROUND: Coronavirus disease 2019 (COVID-19) has become a pandemic since its first appearance in late 2019. Deaths caused by COVID-19 are still increasing day by day and early diagnosis has become crucial. Since current diagnostic methods have man...

Addressing Biodisaster X Threats With Artificial Intelligence and 6G Technologies: Literature Review and Critical Insights.

Journal of medical Internet research
BACKGROUND: With advances in science and technology, biotechnology is becoming more accessible to people of all demographics. These advances inevitably hold the promise to improve personal and population well-being and welfare substantially. It is pa...

Expression-EEG Bimodal Fusion Emotion Recognition Method Based on Deep Learning.

Computational and mathematical methods in medicine
As one of the key issues in the field of emotional computing, emotion recognition has rich application scenarios and important research value. However, the single biometric recognition in the actual scene has the problem of low accuracy of emotion re...

Image-based nutrient estimation for Chinese dishes using deep learning.

Food research international (Ottawa, Ont.)
Food image recognition systems facilitate dietary assessment and in turn track users' dietary behaviors. However, due to the diversity of Chinese food, a quick and accurate food image recognizing is a particularly challenging task. The success of dee...

Using deep learning convolutional neural networks to automatically perform cerebral aqueduct CSF flow analysis.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Since the development of phase-contrast magnetic resonance imaging (PC-MRI), quantification of cerebrospinal fluid (CSF) flow across the cerebral aqueduct has been utilized for diagnosis of conditions such as normal pressure hydrocephalus (NPH). This...