AIMC Topic:
Image Interpretation, Computer-Assisted
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A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas.
Laboratory investigation; a journal of technical methods and pathology
Aug 22, 2020
Radiomics has potential advantages in the noninvasive histopathological and molecular diagnosis of gliomas. We aimed to develop a novel image signature (IS)-based radiomics model to achieve multilayered preoperative diagnosis and prognostic stratific...
Toward automated severe pharyngitis detection with smartphone camera using deep learning networks.
Computers in biology and medicine
Aug 20, 2020
PURPOSE: Severe pharyngitis is frequently associated with inflammations caused by streptococcal pharyngitis, which can cause immune-mediated and post-infectious complications. The recent global pandemic of coronavirus disease (COVID-19) encourages th...
Automated quantification of myocardial tissue characteristics from native T mapping using neural networks with uncertainty-based quality-control.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Aug 20, 2020
BACKGROUND: Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Nat...
Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Aug 18, 2020
Artificial intelligence (AI) will transform every step in the imaging value chain, including interpretive and noninterpretive components. Radiologists should familiarize themselves with AI developments to become leaders in their clinical implementati...
Application of Deep Learning for Early Screening of Colorectal Precancerous Lesions under White Light Endoscopy.
Computational and mathematical methods in medicine
Aug 18, 2020
METHODS: We collected and sorted out the white light endoscopic images of some patients undergoing colonoscopy. The convolutional neural network model is used to detect whether the image contains lesions: CRC, colorectal adenoma (CRA), and colorectal...
Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application.
PloS one
Aug 14, 2020
Bone metastasis is one of the most frequent diseases in prostate cancer; scintigraphy imaging is particularly important for the clinical diagnosis of bone metastasis. Up to date, minimal research has been conducted regarding the application of machin...
Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study.
AJNR. American journal of neuroradiology
Aug 13, 2020
BACKGROUND AND PURPOSE: Posterior fossa tumors are the most common pediatric brain tumors. MR imaging is key to tumor detection, diagnosis, and therapy guidance. We sought to develop an MR imaging-based deep learning model for posterior fossa tumor d...
Variability and reproducibility in deep learning for medical image segmentation.
Scientific reports
Aug 13, 2020
Medical image segmentation is an important tool for current clinical applications. It is the backbone of numerous clinical diagnosis methods, oncological treatments and computer-integrated surgeries. A new class of machine learning algorithm, deep le...
CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions.
Scientific reports
Aug 13, 2020
Cardiac CINE magnetic resonance imaging is the gold-standard for the assessment of cardiac function. Imaging accelerations have shown to enable 3D CINE with left ventricular (LV) coverage in a single breath-hold. However, 3D imaging remains limited t...
Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation.
Human brain mapping
Aug 12, 2020
Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurological disorders. Diffeomorphic registration-based cortical thickness (DiReCT) is a known technique t...
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