AIMC Journal:
IEEE transactions on medical imaging

Showing 421 to 430 of 687 articles

Unpaired Multi-Modal Segmentation via Knowledge Distillation.

IEEE transactions on medical imaging Feb 3, 2020
Multi-modal learning is typically performed with network architectures containing modality-specific layers and shared layers, utilizing co-registered images of different modalities. We propose a novel learning scheme for unpaired cross-modality image...
Algorithms Magnetic Resonance Imaging Neural Networks, Computer
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Deep Neural Networks for Chronological Age Estimation From OPG Images.

IEEE transactions on medical imaging Jan 31, 2020
Chronological age estimation is crucial labour in many clinical procedures, where the teeth have proven to be one of the best estimators. Although some methods to estimate the age from tooth measurements in orthopantomogram (OPG) images have been dev...
Neural Networks, Computer Databases, Factual Humans Aged Young Adult Female Aged, 80 and over Child, Preschool Infant Male Adult Child Adolescent Middle Aged
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Deep Learning-Based Development of Personalized Human Head Model With Non-Uniform Conductivity for Brain Stimulation.

IEEE transactions on medical imaging Jan 27, 2020
Electromagnetic stimulation of the human brain is a key tool for neurophysiological characterization and the diagnosis of several neurological disorders. Transcranial magnetic stimulation (TMS) is a commonly used clinical procedure. However, personal...
Electric Conductivity Head Humans Deep Learning Brain Magnetic Resonance Imaging
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SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network.

IEEE transactions on medical imaging Jan 21, 2020
Computed tomography (CT) is a widely used screening and diagnostic tool that allows clinicians to obtain a high-resolution, volumetric image of internal structures in a non-invasive manner. Increasingly, efforts have been made to improve the image qu...
Signal-To-Noise Ratio Neural Networks, Computer Tomography, X-Ray Computed Image Processing, Computer-Assisted Attention Humans
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Enabling a Single Deep Learning Model for Accurate Gland Instance Segmentation: A Shape-Aware Adversarial Learning Framework.

IEEE transactions on medical imaging Jan 14, 2020
Segmenting gland instances in histology images is highly challenging as it requires not only detecting glands from a complex background but also separating each individual gland instance with accurate boundary detection. However, due to the boundary ...
Deep Learning Histological Techniques
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Biomarker Localization From Deep Learning Regression Networks.

IEEE transactions on medical imaging Jan 9, 2020
Biomarker estimation methods from medical images have traditionally followed a segment-and-measure strategy. Deep-learning regression networks have changed such a paradigm, enabling the direct estimation of biomarkers in databases where segmentation ...
Deep Learning Image Processing, Computer-Assisted Biomarkers Tomography, X-Ray Computed
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Radon Inversion via Deep Learning.

IEEE transactions on medical imaging Jan 6, 2020
The Radon transform is widely used in physical and life sciences, and one of its major applications is in medical X-ray computed tomography (CT), which is significantly important in disease screening and diagnosis. In this paper, we propose a novel r...
Neural Networks, Computer Tomography, X-Ray Computed Humans Radon Deep Learning Image Processing, Computer-Assisted
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Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients.

IEEE transactions on medical imaging Jan 6, 2020
Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict ove...
Genotype Magnetic Resonance Imaging Brain Neoplasms Deep Learning Glioblastoma Humans Phenotype
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Attention by Selection: A Deep Selective Attention Approach to Breast Cancer Classification.

IEEE transactions on medical imaging Dec 24, 2019
Deep learning approaches are widely applied to histopathological image analysis due to the impressive levels of performance achieved. However, when dealing with high-resolution histopathological images, utilizing the original image as input to the de...
Image Processing, Computer-Assisted Databases, Factual Humans Breast Neoplasms Breast Deep Learning Female
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UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation.

IEEE transactions on medical imaging Dec 13, 2019
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extensive archite...
Tomography, X-Ray Computed Image Processing, Computer-Assisted Neural Networks, Computer Magnetic Resonance Imaging
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