19 results match your criteria labels gans

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Synthesizing anonymized and labeled TOF-MRA patches for brain vessel segmentation using generative adversarial networks.

Comput Biol Med 2021 Apr 15;131:104254. Epub 2021 Feb 15.

CLAIM - Charité Lab for AI in Medicine, Charité Universitätsmedizin Berlin, Germany.

Anonymization and data sharing are crucial for privacy protection and acquisition of large datasets for medical image analysis. This is a big challenge, especially for neuroimaging. Here, the brain's unique structure allows for re-identification and thus requires non-conventional anonymization. Read More

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Generating Synthetic Labeled Data from Existing Anatomical Models: An Example with Echocardiography Segmentation.

IEEE Trans Med Imaging 2021 Jan 14;PP. Epub 2021 Jan 14.

Deep learning can bring time savings and increased reproducibility to medical image analysis. However, acquiring training data is challenging due to the time-intensive nature of labeling and high inter-observer variability in annotations. Rather than labeling images, in this work we propose an alternative pipeline where images are generated from existing high-quality annotations using generative adversarial networks (GANs). Read More

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January 2021

Synthetic CT image generation of shape-controlled lung cancer using semi-conditional InfoGAN and its applicability for type classification.

Int J Comput Assist Radiol Surg 2021 Feb 11;16(2):241-251. Epub 2021 Jan 11.

Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu City, 510-1193, Japan.

Purpose: In recent years, convolutional neural network (CNN), an artificial intelligence technology with superior image recognition, has become increasingly popular and frequently used for classification tasks in medical imaging. However, the amount of labelled data available for classifying medical images is often significantly less than that of natural images, and the handling of rare diseases is often challenging. To overcome these problems, data augmentation has been performed using generative adversarial networks (GANs). Read More

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February 2021

Using conditional generative adversarial networks to reduce the effects of latency in robotic telesurgery.

J Robot Surg 2020 Oct 7. Epub 2020 Oct 7.

Florida Atlantic University, Boca Raton, FL, USA.

The introduction of surgical robots brought about advancements in surgical procedures. The applications of remote telesurgery range from building medical clinics in underprivileged areas, to placing robots abroad in military hot-spots where accessibility and diversity of medical experience may be limited. Poor wireless connectivity may result in a prolonged delay, referred to as latency, between a surgeon's input and action which a robot takes. Read More

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October 2020

Wasserstein GANs for MR Imaging: From Paired to Unpaired Training.

IEEE Trans Med Imaging 2021 01 29;40(1):105-115. Epub 2020 Dec 29.

Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this article leverages unpaired adversarial training for reconstruction networks, where the inputs are undersampled k-space and naively reconstructed images from one dataset, and the labels are high-quality images from another dataset. The reconstruction networks consist of a generator which suppresses the input image artifacts, and a discriminator using a pool of (unpaired) labels to adjust the reconstruction quality. Read More

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January 2021

BSD-GAN: Branched Generative Adversarial Network for Scale-Disentangled Representation Learning and Image Synthesis.

IEEE Trans Image Process 2020 Aug 12;PP. Epub 2020 Aug 12.

We introduce BSD-GAN, a novel multi-branch and scale-disentangled training method which enables unconditional Generative Adversarial Networks (GANs) to learn image representations at multiple scales, benefiting a wide range of generation and editing tasks. The key feature of BSD-GAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features. Specifically, each noise vector, as input to the generator network of BSD-GAN, is deliberately split into several sub-vectors, each corresponding to, and is trained to learn, image representations at a particular scale. Read More

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Deep semi-supervised learning for brain tumor classification.

BMC Med Imaging 2020 07 29;20(1):87. Epub 2020 Jul 29.

Inst. of Image Processing and Pattern Recognition, Shanghai Jiao Tong Univ., Shanghai, 200240, China.

Background: This paper addresses issues of brain tumor, glioma, classification from four modalities of Magnetic Resonance Image (MRI) scans (i.e., T1 weighted MRI, T1 weighted MRI with contrast-enhanced, T2 weighted MRI and FLAIR). Read More

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DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation.

Sci Rep 2020 07 6;10(1):11073. Epub 2020 Jul 6.

Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA.

Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the-art techniques rely on convolutional neural networks (CNNs) that use pixel-to-pixel loss to update network parameters. However, stereotactic body radiotherapy (SBRT) dose is often heterogeneous, making it difficult to model using pixel-level loss. Read More

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Advanced Deep Learning Techniques Applied to Automated Femoral Neck Fracture Detection and Classification.

J Digit Imaging 2020 10;33(5):1209-1217

Columbia University Irving Medical Center, 622 West 168th Street, PB 01-301, New York, NY, 10032, USA.

To use deep learning with advanced data augmentation to accurately diagnose and classify femoral neck fractures. A retrospective study of patients with femoral neck fractures was performed. One thousand sixty-three AP hip radiographs were obtained from 550 patients. Read More

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October 2020

Using deep learning to generate synthetic B-mode musculoskeletal ultrasound images.

Comput Methods Programs Biomed 2020 Nov 4;196:105583. Epub 2020 Jun 4.

Norwegian School of Sport Sciences, Oslo, Norway.

Background And Objective: Deep learning approaches are common in image processing, but often rely on supervised learning, which requires a large volume of training images, usually accompanied by hand-crafted labels. As labelled data are often not available, it would be desirable to develop methods that allow such data to be compiled automatically. In this study, we used a Generative Adversarial Network (GAN) to generate realistic B-mode musculoskeletal ultrasound images, and tested the suitability of two automated labelling approaches. Read More

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November 2020

Toward Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs.

IEEE Trans Cybern 2020 Mar 5. Epub 2020 Mar 5.

Face photo-sketch synthesis aims at generating a facial sketch/photo conditioned on a given photo/sketch. It covers wide applications including digital entertainment and law enforcement. Precisely depicting face photos/sketches remains challenging due to the restrictions on structural realism and textural consistency. Read More

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An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection.

IEEE J Biomed Health Inform 2020 08 6;24(8):2303-2314. Epub 2020 Jan 6.

The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches are mainly based on supervised learning with well-paired image-level or voxel-level labels. However, labeling the lesion in medical images is laborious requiring highly specialized knowledge. Read More

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AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network.

Sensors (Basel) 2019 Dec 12;19(24). Epub 2019 Dec 12.

Center for Remote Sensing of Ice Sheets, University of Kansas, Lawrence, KS 66045, USA.

Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Read More

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December 2019

Improved Techniques for Adversarial Discriminative Domain Adaptation.

IEEE Trans Image Process 2019 Nov 6. Epub 2019 Nov 6.

Adversarial discriminative domain adaptation (ADDA) is an efficient framework for unsupervised domain adaptation in image classification, where the source and target domains are assumed to have the same classes, but no labels are available for the target domain. While ADDA has already achieved better training efficiency and competitive accuracy on image classification in comparison to other adversarial based methods, we investigate whether we can improve its performance with a new framework and new loss formulations. Following the framework of semi-supervised GANs, we first extend the discriminator output over the source classes, in order to model the joint distribution over domain and task. Read More

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November 2019

Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification.

IEEE Trans Cybern 2020 Jul 30;50(7):3318-3329. Epub 2019 May 30.

In this paper, we address the hyperspectral image (HSI) classification task with a generative adversarial network and conditional random field (GAN-CRF)-based framework, which integrates a semisupervised deep learning and a probabilistic graphical model, and make three contributions. First, we design four types of convolutional and transposed convolutional layers that consider the characteristics of HSIs to help with extracting discriminative features from limited numbers of labeled HSI samples. Second, we construct semisupervised generative adversarial networks (GANs) to alleviate the shortage of training samples by adding labels to them and implicitly reconstructing real HSI data distribution through adversarial training. Read More

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Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment.

IEEE Trans Med Imaging 2019 09 7;38(9):2211-2218. Epub 2019 Mar 7.

Recent works show that generative adversarial networks (GANs) can be successfully applied to image synthesis and semi-supervised learning, where, given a small labeled database and a large unlabeled database, the goal is to train a powerful classifier. In this paper, we trained a retinal image synthesizer and a semi-supervised learning method for automatic glaucoma assessment using an adversarial model on a small glaucoma-labeled database and a large unlabeled database. Various studies have shown that glaucoma can be monitored by analyzing the optic disc and its surroundings, and for that reason, the images used in this paper were automatically cropped around the optic disc. Read More

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September 2019

Learning Generative Models of Tissue Organization with Supervised GANs.

IEEE Winter Conf Appl Comput Vis 2018 Mar 7;2018:682-690. Epub 2018 May 7.

Robotics Institute, Carnegie Mellon University.

A key step in understanding the spatial organization of cells and tissues is the ability to construct generative models that accurately reflect that organization. In this paper, we focus on building generative models of electron microscope (EM) images in which the positions of cell membranes and mitochondria have been densely annotated, and propose a two-stage procedure that produces realistic images using Generative Adversarial Networks (or GANs) in a supervised way. In the first stage, we synthesize a label "image" given a noise "image" as input, which then provides supervision for EM image synthesis in the second stage. Read More

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Perceptual Adversarial Networks for Image-to-Image Transformation.

IEEE Trans Image Process 2018 08 14;27(8):4066-4079. Epub 2018 May 14.

In this paper, we propose Perceptual Adversarial Networks (PAN) for image-to-image transformations. Different from existing application driven algorithms, PAN provides a generic framework of learning to map from input images to desired images (Fig. 1), such as a rainy image to its de-rained counterpart, object edges to photos, semantic labels to a scenes image, etc. Read More

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Baseline fat-related dietary behaviors of white, Hispanic, and black participants in a cholesterol screening and education project in New England.

J Am Diet Assoc 2003 Jun;103(6):699-706; discussion 706

Institute for Community Health Promotion, Brown University, I Hoppin Street, Providence, RI 02903, USA.

Objective: To examine baseline fat-related dietary behaviors of white, Hispanic, and black participants in Minimal Contact Education for Cholesterol Change, a National Institutes for Health-funded cholesterol screening and education project conducted in New England.

Subjects: A sample of 9,803 participants who joined the study at baseline (n=7,817 white; n=1,425 Hispanic; and n=561 black).

Methods: Participants completed baseline questionnaires that included demographic and psychosocial items as well as the Food Habits Questionnaire, a dietary assessment tool measuring fat-related dietary behaviors. Read More

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