AIMC Topic: Retrospective Studies

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High-field mr diffusion-weighted image denoising using a joint denoising convolutional neural network.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Low signal-to-noise ratio (SNR) has been a major limiting factor for the application of higher-resolution diffusion-weighted imaging (DWI). Most of the conventional denoising models suffer from the drawbacks of shallow feature extraction ...

Preoperative evaluation of tumour consistency in pituitary macroadenomas: a machine learning-based histogram analysis on conventional T2-weighted MRI.

Neuroradiology
PURPOSE: To evaluate the potential value of machine learning (ML)-based histogram analysis (or first-order texture analysis) on T2-weighted magnetic resonance imaging (MRI) for predicting consistency of pituitary macroadenomas (PMA) and to compare it...

A quantitative SVM approach potentially improves the accuracy of magnetic resonance spectroscopy in the preoperative evaluation of the grades of diffuse gliomas.

NeuroImage. Clinical
OBJECTIVES: To investigate the association between proton magnetic resonance spectroscopy (H-MRS) metabolic features and the grade of gliomas, and to establish a machine-learning model to predict the glioma grade.

Predicting childhood obesity using electronic health records and publicly available data.

PloS one
BACKGROUND: Because of the strong link between childhood obesity and adulthood obesity comorbidities, and the difficulty in decreasing body mass index (BMI) later in life, effective strategies are needed to address this condition in early childhood. ...

Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network.

The oncologist
BACKGROUND: Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical fields, we attempt to integrate a well-trained dee...

Machine Learning for Diagnosis of Hematologic Diseases in Magnetic Resonance Imaging of Lumbar Spines.

Scientific reports
We aimed to assess feasibility of a support vector machine (SVM) texture classifier to discriminate pathologic infiltration patterns from the normal bone marrows in MRI. This retrospective study included 467 cases, which were split into a training (n...

Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.

European radiology
OBJECTIVES: Deep learning reconstruction (DLR) is a new reconstruction method; it introduces deep convolutional neural networks into the reconstruction flow. This study was conducted in order to examine the clinical applicability of abdominal ultra-h...

Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: We aimed to evaluate the value of deep learning on positron emission tomography with computed tomography (PET/CT)-based radiomics for individual induction chemotherapy (IC) in advanced nasopharyngeal carcinoma (NPC).