AIMC Journal:
IEEE transactions on medical imaging

Showing 611 to 620 of 699 articles

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

IEEE transactions on medical imaging Mar 7, 2016
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that ha...
Image Interpretation, Computer-Assisted Computed Tomography Angiography Pulmonary Embolism Colonic Polyps Humans Neural Networks, Computer Machine Learning Colonoscopy ROC Curve Diagnostic Imaging
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Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

IEEE transactions on medical imaging Mar 4, 2016
Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging...
Humans Glioma Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Neural Networks, Computer Machine Learning Brain Neoplasms
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Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network.

IEEE transactions on medical imaging Feb 29, 2016
Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep l...
Lung Diseases, Interstitial Algorithms Humans Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Neural Networks, Computer Lung
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Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring.

IEEE transactions on medical imaging Feb 18, 2016
Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlab...
Breast Neoplasms Breast Density Middle Aged Mammography Humans Aged Unsupervised Machine Learning Breast Female Adult Image Interpretation, Computer-Assisted Risk Factors
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AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images.

IEEE transactions on medical imaging Feb 11, 2016
The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases f...
Histocytochemistry Humans Mitosis Breast Neoplasms Crowdsourcing Neural Networks, Computer Video Games Internet Image Interpretation, Computer-Assisted Machine Learning Female
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Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.

IEEE transactions on medical imaging Feb 11, 2016
We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that co...
Humans Neural Networks, Computer Multiple Sclerosis Image Interpretation, Computer-Assisted Brain Machine Learning Imaging, Three-Dimensional Algorithms Magnetic Resonance Imaging
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Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.

IEEE transactions on medical imaging Feb 11, 2016
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image fea...
Humans Lung Diseases, Interstitial Image Interpretation, Computer-Assisted Neural Networks, Computer Diagnosis, Computer-Assisted Databases, Factual Lymph Nodes Reproducibility of Results
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Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks.

IEEE transactions on medical imaging Feb 11, 2016
Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelle...
Cerebral Hemorrhage Image Interpretation, Computer-Assisted Neural Networks, Computer Databases, Factual Biomarkers Brain Imaging, Three-Dimensional Magnetic Resonance Imaging Humans
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Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images.

IEEE transactions on medical imaging Feb 8, 2016
Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is...
Humans Neural Networks, Computer Machine Learning Retinal Hemorrhage Databases, Factual Fundus Oculi Image Interpretation, Computer-Assisted
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Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines.

IEEE transactions on medical imaging Feb 8, 2016
The choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning m...
Humans Neural Networks, Computer Tomography, X-Ray Computed Lung Image Processing, Computer-Assisted Machine Learning Algorithms
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