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Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images.

Physics in medicine and biology
 Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate segmentation of the primary tumor (GTVp) of oropharyngeal cancer patients (OPC) each image volume is explored slice-by-slice from different orientati...

Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients.

BMC bioinformatics
BACKGROUND: Lung cancer is the leading cause of cancer-related deaths worldwide. The majority of lung cancers are non-small cell lung cancer (NSCLC), accounting for approximately 85% of all lung cancer types. The Cox proportional hazards model (CPH),...

Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application.

Physics in medicine and biology
. In PET/CT imaging, CT is used for positron emission tomography (PET) attenuation correction (AC). CT artifacts or misalignment between PET and CT can cause AC artifacts and quantification errors in PET. Simultaneous reconstruction (MLAA) of PET act...

Low-count whole-body PET/MRI restoration: an evaluation of dose reduction spectrum and five state-of-the-art artificial intelligence models.

European journal of nuclear medicine and molecular imaging
PURPOSE: To provide a holistic and complete comparison of the five most advanced AI models in the augmentation of low-dose F-FDG PET data over the entire dose reduction spectrum.

A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on F-FDG PET/CT images.

Japanese journal of radiology
PURPOSE: To explore a multidomain fusion model of radiomics and deep learning features based on F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) images to distinguish pancreatic ductal adenocarcinoma (PDAC) and aut...

Multi-stage classification of Alzheimer's disease from F-FDG-PET images using deep learning techniques.

Physical and engineering sciences in medicine
The study aims to implement a convolutional neural network framework that uses the 18F-FDG PET modality of brain imaging to detect multiple stages of dementia, including Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI)...

Deep learning signature of brain [F]FDG PET associated with cognitive outcome of rapid eye movement sleep behavior disorder.

Scientific reports
An objective biomarker to predict the outcome of isolated rapid eye movement sleep behavior disorder (iRBD) is crucial for the management. This study aimed to investigate cognitive signature of brain [F]FDG PET based on deep learning (DL) for evaluat...

Opportunistic deep learning powered calcium scoring in oncologic patients with very high coronary artery calcium (≥ 1000) undergoing 18F-FDG PET/CT.

Scientific reports
Our aim was to identify and quantify high coronary artery calcium (CAC) with deep learning (DL)-powered CAC scoring (CACS) in oncological patients with known very high CAC (≥ 1000) undergoing 18F-FDG-PET/CT for re-/staging. 100 patients were enrolled...

Fully Decoupled Neural Network Learning Using Delayed Gradients.

IEEE transactions on neural networks and learning systems
Training neural networks with backpropagation (BP) requires a sequential passing of activations and gradients. This has been recognized as the lockings (i.e., the forward, backward, and update lockings) among modules (each module contains a stack of ...

Development of a deep learning network for Alzheimer's disease classification with evaluation of imaging modality and longitudinal data.

Physics in medicine and biology
. Neuroimaging uncovers important information about disease in the brain. Yet in Alzheimer's disease (AD), there remains a clear clinical need for reliable tools to extract diagnoses from neuroimages. Significant work has been done to develop deep le...