AIMC Journal:
Medical image analysis

Showing 371 to 380 of 684 articles

Active, continual fine tuning of convolutional neural networks for reducing annotation efforts.

Medical image analysis Mar 24, 2021
The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places. However, in medical imaging, it is challenging to create such larg...
Humans Neural Networks, Computer Longitudinal Studies Diagnostic Imaging
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Loss odyssey in medical image segmentation.

Medical image analysis Mar 19, 2021
The loss function is an important component in deep learning-based segmentation methods. Over the past five years, many loss functions have been proposed for various segmentation tasks. However, a systematic study of the utility of these loss functio...
Humans Neural Networks, Computer Image Processing, Computer-Assisted
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Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping.

Medical image analysis Mar 11, 2021
Recent developments in artificial intelligence have generated increasing interest to deploy automated image analysis for diagnostic imaging and large-scale clinical applications. However, inaccuracy from automated methods could lead to incorrect conc...
Humans Neural Networks, Computer Artificial Intelligence Reproducibility of Results Image Processing, Computer-Assisted Magnetic Resonance Imaging Quality Control
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Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides.

Medical image analysis Mar 10, 2021
Feature vectors provided by pre-trained deep artificial neural networks have become a dominant source for image representation in recent literature. Their contribution to the performance of image analysis can be improved through fine-tuning. As an ul...
Humans Neural Networks, Computer Neoplasms Image Processing, Computer-Assisted
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Ultrasound image reconstruction from plane wave radio-frequency data by self-supervised deep neural network.

Medical image analysis Feb 25, 2021
Image reconstruction from radio-frequency (RF) data is crucial for ultrafast plane wave ultrasound (PWUS) imaging. Compared with the traditional delay-and-sum (DAS) method based on relatively imprecise assumptions, sparse regularization (SR) method d...
Humans Neural Networks, Computer Deep Learning Ultrasonography Image Processing, Computer-Assisted
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Deep Consensus Network: Aggregating predictions to improve object detection in microscopy images.

Medical image analysis Feb 24, 2021
Detection of cells and particles in microscopy images is a common and challenging task. In recent years, detection approaches in computer vision achieved remarkable improvements by leveraging deep learning. Microscopy images pose challenges like smal...
Neural Networks, Computer Algorithms Consensus Bayes Theorem Microscopy
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Deep virtual adversarial self-training with consistency regularization for semi-supervised medical image classification.

Medical image analysis Feb 22, 2021
Convolutional neural networks have achieved prominent success on a variety of medical imaging tasks when a large amount of labeled training data is available. However, the acquisition of expert annotations for medical data is usually expensive and ti...
Humans Neural Networks, Computer Female Breast Neoplasms Ultrasonography Tomography, Optical Coherence Supervised Machine Learning
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Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy.

Medical image analysis Feb 17, 2021
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies...
Humans Algorithms Deep Learning Endoscopy, Gastrointestinal Artifacts
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Learning to map 2D ultrasound images into 3D space with minimal human annotation.

Medical image analysis Feb 16, 2021
In fetal neurosonography, aligning two-dimensional (2D) ultrasound scans to their corresponding plane in the three-dimensional (3D) space remains a challenging task. In this paper, we propose a convolutional neural network that predicts the position ...
Humans Neural Networks, Computer Ultrasonography Image Processing, Computer-Assisted Neuroimaging Gestational Age
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Estimating dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network.

Medical image analysis Feb 16, 2021
Dual-energy computed tomography (DECT) is of great significance for clinical practice due to its huge potential to provide material-specific information. However, DECT scanners are usually more expensive than standard single-energy CT (SECT) scanners...
Humans Neural Networks, Computer Tomography, X-Ray Computed
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