AIMC Topic: Deep Learning

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Quantitative susceptibility mapping in magnetically inhomogeneous tissues.

Magnetic resonance in medicine
PURPOSE: Conventional quantitative susceptibility mapping (QSM) methods rely on simplified physical models that assume isotropic and homogeneous tissue properties, leading to artifacts and inaccuracies in biological tissues. This study aims to develo...

TabNet and TabTransformer: Novel Deep Learning Models for Chemical Toxicity Prediction in Comparison With Machine Learning.

Journal of applied toxicology : JAT
The prediction of chemical toxicity is crucial for applications in drug discovery, environmental safety, and regulatory assessments. This study aims to evaluate the performance of advanced deep learning architectures, TabNet and TabTransformer, in co...

Deep Learning in Echocardiography for Enhanced Detection of Left Ventricular Function and Wall Motion Abnormalities.

Ultrasound in medicine & biology
Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, underscoring the need for advancements in diagnostic methodologies to improve early detection and treatment outcomes. This systematic review examines the integration of adv...

A deep learning algorithm for automated adrenal gland segmentation on non-contrast CT images.

BMC medical imaging
BACKGROUND: The adrenal glands are small retroperitoneal organs, few reference standards exist for adrenal CT measurements in clinical practice. This study aims to develop a deep learning (DL) model for automated adrenal gland segmentation on non-con...

Artifact estimation network for MR images: effectiveness of batch normalization and dropout layers.

BMC medical imaging
BACKGROUND: Magnetic resonance imaging (MRI) is an essential tool for medical diagnosis. However, artifacts may degrade images obtained through MRI, especially owing to patient movement. Existing methods that mitigate the artifact problem are subject...

Assessing english Language teachers' pedagogical effectiveness using convolutional neural networks optimized by modified virus colony search algorithm.

Scientific reports
Effective teacher performance evaluation is important for enhancing the quality of educational systems. This study presents a novel approach that integrates deep learning and metaheuristics to assess the pedagogical quality of English as a foreign la...

A human pose estimation network based on YOLOv8 framework with efficient multi-scale receptive field and expanded feature pyramid network.

Scientific reports
Deep neural networks are used to accurately detect, estimate, and predict human body poses in images or videos through deep learning-based human pose estimation. However, traditional multi-person pose estimation methods face challenges due to partial...

Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel.

BMC genomics
BACKGROUND: Accurate genetic risk prediction and understanding the mechanisms underlying complex diseases are essential for effective intervention and precision medicine. However, current methods often struggle to capture the intricate and subtle gen...

Deep Learning Model of Primary Tumor and Metastatic Cervical Lymph Nodes From CT for Outcome Predictions in Oropharyngeal Cancer.

JAMA network open
IMPORTANCE: Primary tumor (PT) and metastatic cervical lymph node (LN) characteristics are highly associated with oropharyngeal squamous cell carcinoma (OPSCC) prognosis. Currently, there is a lack of studies to combine imaging characteristics of bot...

A deep learning-based clinical-radiomics model predicting the treatment response of immune checkpoint inhibitors (ICIs)-based conversion therapy in potentially convertible hepatocelluar carcinoma patients: a tumor marker prognostic study.

International journal of surgery (London, England)
BACKGROUND: The majority of patients with hepatocellular carcinoma (HCC) miss the opportunity of radical resection, making immune check-point inhibitors (ICIs)-based conversion therapy a primary option. However, challenges persist in predicting respo...