AIMC Topic: Deep Learning

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A Deep Learning Model with High Standalone Performance for Diagnosis of Unruptured Intracranial Aneurysm.

Yonsei medical journal
PURPOSE: This study aimed to investigate whether a deep learning model for automated detection of unruptured intracranial aneurysms on time-of-flight (TOF) magnetic resonance angiography (MRA) can achieve a target diagnostic performance comparable to...

Fully Automated Gross Tumor Volume Delineation From PET in Head and Neck Cancer Using Deep Learning Algorithms.

Clinical nuclear medicine
PURPOSE: The availability of automated, accurate, and robust gross tumor volume (GTV) segmentation algorithms is critical for the management of head and neck cancer (HNC) patients. In this work, we evaluated 3 state-of-the-art deep learning algorithm...

Updates in deep learning research in ophthalmology.

Clinical science (London, England : 1979)
Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular ima...

Factors determining generalization in deep learning models for scoring COVID-CT images.

Mathematical biosciences and engineering : MBE
The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in ...

[Establishment of a deep learning-based visual model for intelligent recognition of ].

Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control
OBJECTIVE: To establish a deep learning-based visual model for intelligent recognition of , the intermediate host of , and evaluate the effects of different training strategies for image recognition.

[Deep learning reconstruction algorithm for coronary CT angiography in assessing obstructive coronary artery disease caused by calcified lesions: the clinical application value].

Zhonghua yi xue za zhi
To investigate the image quality of coronary CT angiography (CCTA) subjected to deep learning-based reconstruction algorithm (DLR) method and its diagnostic performance for stenosis caused by coronary calcified lesions. We enrolled 33 consecutive p...

Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training.

Proceedings of the National Academy of Sciences of the United States of America
In this paper, we introduce the , a nonconvex, yet analytically tractable, optimization program, in a quest to better understand deep neural networks that are trained for a sufficiently long time. As the name suggests, this model is derived by isolat...

A deep learning algorithm for detecting acute myocardial infarction.

EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology
BACKGROUND: Delayed diagnosis or misdiagnosis of acute myocardial infarction (AMI) is not unusual in daily practice. Since a 12-lead electrocardiogram (ECG) is crucial for the detection of AMI, a systematic algorithm to strengthen ECG interpretation ...