AIMC Topic: Deep Learning

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Automatic quantification of scapular and glenoid morphology from CT scans using deep learning.

European journal of radiology
OBJECTIVES: To develop and validate an open-source deep learning model for automatically quantifying scapular and glenoid morphology using CT images of normal subjects and patients with glenohumeral osteoarthritis.

Skin-CAD: Explainable deep learning classification of skin cancer from dermoscopic images by feature selection of dual high-level CNNs features and transfer learning.

Computers in biology and medicine
Skin cancer (SC) significantly impacts many individuals' health all over the globe. Hence, it is imperative to promptly identify and diagnose such conditions at their earliest stages using dermoscopic imaging. Computer-aided diagnosis (CAD) methods r...

Deep learning-based localization algorithms on fluorescence human brain 3D reconstruction: a comparative study using stereology as a reference.

Scientific reports
3D reconstruction of human brain volumes at high resolution is now possible thanks to advancements in tissue clearing methods and fluorescence microscopy techniques. Analyzing the massive data produced with these approaches requires automatic methods...

A retrospective study of deep learning generalization across two centers and multiple models of X-ray devices using COVID-19 chest-X rays.

Scientific reports
Generalization of deep learning (DL) algorithms is critical for the secure implementation of computer-aided diagnosis systems in clinical practice. However, broad generalization remains to be a challenge in machine learning. This research aims to ide...

CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI.

Scientific data
Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality...

AI nutrition recommendation using a deep generative model and ChatGPT.

Scientific reports
In recent years, major advances in artificial intelligence (AI) have led to the development of powerful AI systems for use in the field of nutrition in order to enhance personalized dietary recommendations and improve overall health and well-being. H...

Deep learning model for the prediction of all-cause mortality among long term care people in China: a prospective cohort study.

Scientific reports
This study aimed to develop a deep learning model to predict the risk stratification of all-cause death for older people with disability, providing guidance for long-term care plans. Based on the government-led long-term care insurance program in a p...

Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery.

Nature communications
Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles ...

An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology.

BMC medical imaging
BACKGROUND: The assessment of in vitro wound healing images is critical for determining the efficacy of the therapy-of-interest that may influence the wound healing process. Existing methods suffer significant limitations, such as user dependency, ti...

Factor-GAN: Enhancing stock price prediction and factor investment with Generative Adversarial Networks.

PloS one
Deep learning, a pivotal branch of artificial intelligence, has increasingly influenced the financial domain with its advanced data processing capabilities. This paper introduces Factor-GAN, an innovative framework that utilizes Generative Adversaria...