AIMC Topic: Multimodal Imaging
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Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI.
European journal of nuclear medicine and molecular imaging
Jul 1, 2019
OBJECTIVE: Quantitative PET/MR imaging is challenged by the accuracy of synthetic CT (sCT) generation from MR images. Deep learning-based algorithms have recently gained momentum for a number of medical image analysis applications. In this work, a no...
Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks.
IEEE journal of biomedical and health informatics
Jun 14, 2019
In this paper, we propose a bi-modality medical image synthesis approach based on sequential generative adversarial network (GAN) and semi-supervised learning. Our approach consists of two generative modules that synthesize images of the two modaliti...
A Transition Control Mechanism for Artificial Bee Colony (ABC) Algorithm.
Computational intelligence and neuroscience
Apr 1, 2019
Artificial Bee Colony (ABC) algorithm inspired by the complex search and foraging behaviors of real honey bees is one of the most promising implementations of the Swarm Intelligence- (SI-) based optimization algorithms. Due to its robust and phase-di...
Prediction of molecular subtypes of breast cancer using BI-RADS features based on a "white box" machine learning approach in a multi-modal imaging setting.
European journal of radiology
Mar 21, 2019
PURPOSE: To develop and validate an interpretable and repeatable machine learning model approach to predict molecular subtypes of breast cancer from clinical metainformation together with mammography and MRI images.
Nasopharyngeal carcinoma segmentation based on enhanced convolutional neural networks using multi-modal metric learning.
Physics in medicine and biology
Jan 8, 2019
Multi-modality examinations have been extensively applied in current clinical cancer management. Leveraging multi-modality medical images can be highly beneficial for automated tumor segmentation as they provide complementary information that could m...
MRI-based attenuation correction for brain PET/MRI based on anatomic signature and machine learning.
Physics in medicine and biology
Jan 7, 2019
Deriving accurate attenuation maps for PET/MRI remains a challenging problem because MRI voxel intensities are not related to properties of photon attenuation and bone/air interfaces have similarly low signal. This work presents a learning-based meth...
Combining multimodal imaging and treatment features improves machine learning-based prognostic assessment in patients with glioblastoma multiforme.
Cancer medicine
Dec 18, 2018
BACKGROUND: For Glioblastoma (GBM), various prognostic nomograms have been proposed. This study aims to evaluate machine learning models to predict patients' overall survival (OS) and progression-free survival (PFS) on the basis of clinical, patholog...
Ultra-Low-Dose F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs.
Radiology
Dec 11, 2018
Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Methods Forty data sets from 39 patients (mean age ± standard deviation [SD], 67 years ± 8), including...
Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis.
Human brain mapping
Nov 1, 2018
In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive Impairment (MCI), from normal aging subjects. Multimodality neuroimagi...
HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation.
IEEE transactions on medical imaging
Oct 30, 2018
Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, DenseNet that connects each layer to every other layer in a feed-fo...
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