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Showing 951 to 960 of 1686 articles

Super-resolution technology to simultaneously improve optical & digital resolution of optical coherence tomography via deep learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment. In cardiac imaging, OCT has been used in assessing plaques before and after stenting. While needed in many scenarios, high resolution comes...
Image Processing, Computer-Assisted Neural Networks, Computer Deep Learning Tomography, Optical Coherence Humans
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Prediction for Morphology and States of Stem Cell Colonies using a LSTM Network with Progressive Training Microscopy Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
We present a new LSTM (P-LSTM: Progressive LSTM) network, aiming to predict morphology and states of cell colonies from time-lapse microscopy images. Apparent short-term changes occur in some types of time-lapse cell images. Therefore, long-term-memo...
Algorithms Stem Cells Neural Networks, Computer Memory, Long-Term Microscopy Humans
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Training Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Using medical images recorded in clinical practice has the potential to be a game-changer in the application of machine learning for medical decision support. Thousands of medical images are produced in daily clinical activity. The diagnosis of medic...
Magnetic Resonance Spectroscopy Meningeal Neoplasms Machine Learning Humans Magnetic Resonance Imaging Neural Networks, Computer
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Efficient Deep Learning-based Wound-bed Segmentation For Mobile Applications.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
This paper proposes a deep learning image segmentation method for the purpose of segmenting wound-bed regions from the background. Our contributions include proposing a fast and efficient convolutional neural networks (CNN)-based segmentation network...
Neural Networks, Computer Image Processing, Computer-Assisted Mobile Applications Deep Learning
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Extracting Membrane Borders in IVUS Images Using a Multi-Scale Feature Aggregated U-Net.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Automatic extraction of the lumen-intima border (LIB) and the media-adventitia border (MAB) in intravascular ultrasound (IVUS) images is of high clinical interest. Despite the superior performance achieved by deep neural networks (DNNs) on various me...
Ultrasonography Neural Networks, Computer Membranes Adventitia
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Deep Learning with Skip Connection Attention for Choroid Layer Segmentation in OCT Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Since the thickness and shape of the choroid layer are indicators for the diagnosis of several ophthalmic diseases, the choroid layer segmentation is an important task. There exist many challenges in segmentation of the choroid layer. In this paper, ...
Attention Data Collection Choroid Tomography, Optical Coherence Deep Learning
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MRI-SegFlow: a novel unsupervised deep learning pipeline enabling accurate vertebral segmentation of MRI images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Most deep learning based vertebral segmentation methods require laborious manual labelling tasks. We aim to establish an unsupervised deep learning pipeline for vertebral segmentation of MR images. We integrate the sub-optimal segmentation results pr...
Printing, Three-Dimensional Spine Magnetic Resonance Imaging Deep Learning
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UENet: A Novel Generative Adversarial Network for Angiography Image Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
Convolutional neural networks (CNNs) have been widely used in medical image segmentation. Vessel segmentation in coronary angiography remains a challenging task. It is a great challenge to extract fine features of coronary artery for segmentation due...
Image Processing, Computer-Assisted Semantics Neural Networks, Computer Angiography
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Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator t...
Machine Learning Skin Neoplasms Learning Imagery, Psychotherapy Humans Neural Networks, Computer
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3-To-1 Pipeline: Restructuring Transfer Learning Pipelines for Medical Imaging Classification via Optimized GAN Synthetic Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2020
The difficulty of applying deep learning algorithms to biomedical imaging systems arises from a lack of training images. An existing workaround to the lack of medical training images involves pre-training deep learning models on ImageNet, a non-medic...
Humans Algorithms Radiography X-Rays Magnetic Resonance Imaging Deep Learning
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