AIMC Journal:
IEEE transactions on medical imaging

Showing 381 to 390 of 687 articles

Lesion-Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale.

IEEE transactions on medical imaging Dec 29, 2020
The acquisition of large-scale medical image data, necessary for training machine learning algorithms, is hampered by associated expert-driven annotation costs. Mining hospital archives can address this problem, but labels often incomplete or noisy, ...
Machine Learning Algorithms
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Direct Quantification of Coronary Artery Stenosis Through Hierarchical Attentive Multi-View Learning.

IEEE transactions on medical imaging Nov 30, 2020
Quantification of coronary artery stenosis on X-ray angiography (XRA) images is of great importance during the intraoperative treatment of coronary artery disease. It serves to quantify the coronary artery stenosis by estimating the clinical morpholo...
Humans Deep Learning Coronary Artery Disease Coronary Stenosis
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Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network.

IEEE transactions on medical imaging Nov 30, 2020
Fully convolutional neural networks have made promising progress in joint liver and liver tumor segmentation. Instead of following the debates over 2D versus 3D networks (for example, pursuing the balance between large-scale 2D pretraining and 3D con...
Humans Neural Networks, Computer Liver Neoplasms Image Processing, Computer-Assisted Organs at Risk
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Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images.

IEEE transactions on medical imaging Nov 30, 2020
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. However, this rotational symmetry is not widely utilised as prior knowledge in modern Convolutional Neural Networks (CNNs), resulting in ...
Neural Networks, Computer Image Processing, Computer-Assisted Histological Techniques
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Zoom in Lesions for Better Diagnosis: Attention Guided Deformation Network for WCE Image Classification.

IEEE transactions on medical imaging Nov 30, 2020
Wireless capsule endoscopy (WCE) is a novel imaging tool that allows noninvasive visualization of the entire gastrointestinal (GI) tract without causing discomfort to patients. Convolutional neural networks (CNNs), though perform favorably against tr...
Humans Neural Networks, Computer Machine Learning Software Attention Capsule Endoscopy
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Target-Independent Domain Adaptation for WBC Classification Using Generative Latent Search.

IEEE transactions on medical imaging Nov 30, 2020
Automating the classification of camera-obtained microscopic images of White Blood Cells (WBCs) and related cell subtypes has assumed importance since it aids the laborious manual process of review and diagnosis. Several State-Of-The-Art (SOTA) metho...
Neural Networks, Computer Algorithms Models, Theoretical Leukocytes
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Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods.

IEEE transactions on medical imaging Nov 30, 2020
Medical imaging systems are commonly assessed and optimized by use of objective measures of image quality (IQ). The Ideal Observer (IO) performance has been advocated to provide a figure-of-merit for use in assessing and optimizing imaging systems be...
ROC Curve Image Processing, Computer-Assisted Supervised Machine Learning Markov Chains Monte Carlo Method Normal Distribution
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Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images.

IEEE transactions on medical imaging Nov 30, 2020
In this study, we propose a fast and accurate method to automatically localize anatomical landmarks in medical images. We employ a global-to-local localization approach using fully convolutional neural networks (FCNNs). First, a global FCNN localizes...
Neural Networks, Computer Algorithms Reproducibility of Results Deep Learning Anatomic Landmarks
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Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation.

IEEE transactions on medical imaging Nov 30, 2020
Segmenting anatomical structures in medical images has been successfully addressed with deep learning methods for a range of applications. However, this success is heavily dependent on the quality of the image that is being segmented. A commonly negl...
Humans Deep Learning Image Processing, Computer-Assisted Heart Motion Artifacts
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Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis.

IEEE transactions on medical imaging Nov 30, 2020
The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decision-making. However, developing such automatic solutions is challenging due to the requirement of a large amount of human-annotated data. Rec...
Humans Neural Networks, Computer Retinal Diseases
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