AIMC Topic: Humans

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Multi-planar 3D breast segmentation in MRI via deep convolutional neural networks.

Artificial intelligence in medicine
Nowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated to be a valid complementary diagnostic tool for early detection and diagnosis of breast cancer. However, without a CAD (Computer Aided Detection) system, manual...

DW-Net: A cascaded convolutional neural network for apical four-chamber view segmentation in fetal echocardiography.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Fetal echocardiography (FE) is a widely used medical examination for early diagnosis of congenital heart disease (CHD). The apical four-chamber view (A4C) is an important view among early FE images. Accurate segmentation of crucial anatomical structu...

One-slice CT image based kernelized radiomics model for the prediction of low/mid-grade and high-grade HNSCC.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
An accurate grade prediction can help to appropriate treatment strategy and effective diagnosis to Head and neck squamous cell carcinoma (HNSCC). Radiomics has been studied for the prediction of carcinoma characteristics in medical images. The succes...

High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Cancer immunology research
Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on estimation of MHC binding aff...

Deep learning of diffraction image patterns for accurate classification of five cell types.

Journal of biophotonics
Development of label-free methods for accurate classification of cells with high throughput can yield powerful tools for biological research and clinical applications. We have developed a deep neural network of DINet for extracting features from cros...

Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA.

Atherosclerosis
BACKGROUND AND AIMS: Artificial intelligence (AI) is increasing its role in diagnosis of patients with suspicious coronary artery disease. The aim of this manuscript is to develop a deep convolutional neural network (CNN) to classify coronary compute...

Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images.

Medical image analysis
The tracking of the knee femoral condyle cartilage during ultrasound-guided minimally invasive procedures is important to avoid damaging this structure during such interventions. In this study, we propose a new deep learning method to track, accurate...

Fully automatic estimation of pelvic sagittal inclination from anterior-posterior radiography image using deep learning framework.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Malposition of the acetabular component causes dislocation and prosthetic impingement after Total Hip Arthroplasty (THA), which significantly affects the postoperative quality of life and implant longevity. The position of t...

Application of Machine Learning for Predicting Clinically Meaningful Outcome After Arthroscopic Femoroacetabular Impingement Surgery.

The American journal of sports medicine
BACKGROUND: Hip arthroscopy has become an important tool for surgical treatment of intra-articular hip pathology. Predictive models for clinically meaningful outcomes in patients undergoing hip arthroscopy for femoroacetabular impingement syndrome (F...