AIMC Journal:
IEEE transactions on medical imaging

Showing 411 to 420 of 687 articles

Nondestructive Detection of Targeted Microbubbles Using Dual-Mode Data and Deep Learning for Real-Time Ultrasound Molecular Imaging.

IEEE transactions on medical imaging Apr 9, 2020
Ultrasound molecular imaging (UMI) is enabled by targeted microbubbles (MBs), which are highly reflective ultrasound contrast agents that bind to specific biomarkers. Distinguishing between adherent MBs and background signals can be challenging in vi...
Humans Animals Deep Learning Ultrasonography Mice Contrast Media Molecular Imaging Microbubbles
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CPFNet: Context Pyramid Fusion Network for Medical Image Segmentation.

IEEE transactions on medical imaging Mar 27, 2020
Accurate and automatic segmentation of medical images is a crucial step for clinical diagnosis and analysis. The convolutional neural network (CNN) approaches based on the U-shape structure have achieved remarkable performances in many different medi...
Neural Networks, Computer Image Processing, Computer-Assisted
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ψ-Net: Stacking Densely Convolutional LSTMs for Sub-Cortical Brain Structure Segmentation.

IEEE transactions on medical imaging Feb 24, 2020
Sub-cortical brain structure segmentation is of great importance for diagnosing neuropsychiatric disorders. However, developing an automatic approach to segmenting sub-cortical brain structures remains very challenging due to the ambiguous boundaries...
Neural Networks, Computer Brain Image Processing, Computer-Assisted
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Block Level Skip Connections Across Cascaded V-Net for Multi-Organ Segmentation.

IEEE transactions on medical imaging Feb 21, 2020
Multi-organ segmentation is a challenging task due to the label imbalance and structural differences between different organs. In this work, we propose an efficient cascaded V-Net model to improve the performance of multi-organ segmentation by establ...
Neural Networks, Computer Algorithms Tomography, X-Ray Computed
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Multi-Contrast Super-Resolution MRI Through a Progressive Network.

IEEE transactions on medical imaging Feb 18, 2020
Magnetic resonance imaging (MRI) is widely used for screening, diagnosis, image-guided therapy, and scientific research. A significant advantage of MRI over other imaging modalities such as computed tomography (CT) and nuclear imaging is that it clea...
Neural Networks, Computer Tomography, X-Ray Computed Magnetic Resonance Imaging Signal-To-Noise Ratio
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MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data.

IEEE transactions on medical imaging Feb 17, 2020
Automated prostate segmentation in MRI is highly demanded for computer-assisted diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress in this task, usually relying on large amounts of training data. Due to the natu...
Humans Male Diagnosis, Computer-Assisted Deep Learning Magnetic Resonance Imaging Prostate
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Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation.

IEEE transactions on medical imaging Feb 12, 2020
Recent advances in deep learning for medical image segmentation demonstrate expert-level accuracy. However, application of these models in clinically realistic environments can result in poor generalization and decreased accuracy, mainly due to the d...
Humans Male Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation.

IEEE transactions on medical imaging Feb 10, 2020
Unsupervised domain adaptation has increasingly gained interest in medical image computing, aiming to tackle the performance degradation of deep neural networks when being deployed to unseen data with heterogeneous characteristics. In this work, we p...
Neural Networks, Computer Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Unsupervised Domain Adaptation to Classify Medical Images Using Zero-Bias Convolutional Auto-Encoders and Context-Based Feature Augmentation.

IEEE transactions on medical imaging Feb 3, 2020
The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale labelled training data. In medical imaging, these large labelled datasets are sparse, mainly related to the complexity ...
Neural Networks, Computer Diagnostic Imaging
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Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images.

IEEE transactions on medical imaging Feb 3, 2020
Digital histology images are amenable to the application of convolutional neural networks (CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. 224×...
Humans Neural Networks, Computer Colorectal Neoplasms Histological Techniques
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