AIMC Topic: Deep Learning

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A deployment safety case for AI-assisted prostate cancer diagnosis.

Computers in biology and medicine
Deep learning (DL) has the potential to deliver significant clinical benefits. In recent years, an increasing number of DL-based systems have been approved by the relevant regulators, e.g. FDA. Although obtaining regulatory approvals is a prerequisit...

Sustainable water allocation under climate change: Deep learning approaches to predict drinking water shortages.

Journal of environmental management
Addressing sustainable urban water supply has become one of the most critical challenges for modern megacities, particularly in arid and semi-arid regions where rapid urbanization and climate change converge to exacerbate resource scarcity. Tehran, a...

A multi-model deep learning approach for the identification of coronary artery calcifications within 2D coronary angiography images.

International journal of computer assisted radiology and surgery
PURPOSE: Identifying and quantifying coronary artery calcification (CAC) is crucial for preoperative planning, as it helps to estimate both the complexity of the 2D coronary angiography (2DCA) procedure and the risk of developing intraoperative compl...

Interpretable deep neural networks for advancing early neonatal birth weight prediction using multimodal maternal factors.

Journal of biomedical informatics
BACKGROUND: Neonatal low birth weight (LBW) is a significant predictor of increased morbidity and mortality among newborns. Predominantly, traditional prediction methods depend heavily on ultrasonography, which does not consider risk factors affectin...

AutoFE-Pointer: Auto-weighted feature extractor based on pointer network for DNA methylation prediction.

International journal of biological macromolecules
DNA methylation is a critical epigenetic modification that plays a central role in gene regulation, cellular differentiation, and the development of various diseases, including cancers. Aberrant methylation patterns have emerged as both biomarkers an...

Improving microsurgical suture training with automated phase recognition and skill assessment via deep learning.

Computers in biology and medicine
Microsurgical suturing demands a high level of precision, skill, and extensive training to ensure success in delicate procedures. In this study, we created a deep-learning approach for automating phase recognition and skill assessment in microsurgica...

Review learning: Real world validation of privacy preserving continual learning across medical institutions.

Computers in biology and medicine
When a deep learning model is trained sequentially on different datasets, it often forgets the knowledge learned from previous data, a problem known as catastrophic forgetting. This damages the model's performance on diverse datasets, which is critic...

Spotlights on novel strategic innovations on the artificial intelligence and deep learning driven quality control focuses in transfusion medicine, to optimize blood component safety and efficacy and minimize the potential pitfalls.

Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis
Artificial intelligence (AI) combined with human intelligent, and machine learning (ML) are transforming quality control (QC) in transfusion medicine, enhancing efficiency, accuracy, and transfusion clinical safety. Traditional QC methods require ext...

Real-time brain tumour diagnoses using a novel lightweight deep learning model.

Computers in biology and medicine
Brain tumours continue to be a primary cause of worldwide death, highlighting the critical need for effective and accurate diagnostic tools. This article presents MK-YOLOv8, an innovative lightweight deep learning framework developed for the real-tim...

An Optimized Framework of QSM Mask Generation Using Deep Learning: QSMmask-Net.

NMR in biomedicine
Quantitative susceptibility mapping (QSM) provides the spatial distribution of magnetic susceptibility within tissues through sequential steps: phase unwrapping and echo combination, mask generation, background field removal, and dipole inversion. Ac...