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Uncertainty-Guided Progressive GANs for Medical Image Translation
[article]
2021
arXiv
pre-print
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In this work, we propose an uncertainty-guided progressive learning scheme for image-to-image translation. ...
By incorporating aleatoric uncertainty as attention maps for GANs trained in a progressive manner, we generate images of increasing fidelity progressively. ...
The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for support. ...
arXiv:2106.15542v2
fatcat:tm2xihczzrbs7cbhe7bq5k6nbe
A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions
[article]
2021
arXiv
pre-print
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The recent advancements in Generative Adversarial Networks (GANs) in computer vision as well as in medical imaging may provide a basis for enhanced capabilities in cancer detection and analysis. ...
Despite technological and medical advances, the detection, interpretation, and treatment of cancer based on imaging data continue to pose significant challenges. ...
For example, by comparing the original patient image after progression with the GAN-generated predicted image (or its latent representation) after progression for time spans of interest. ...
arXiv:2107.09543v1
fatcat:jz76zqklpvh67gmwnsdqzgq5he
Front Matter: Volume 12032
2022
Medical Imaging 2022: Image Processing
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1Y Progressive GANomaly: anomaly detection with progressively growing GANs [12032-67] 1Z Image quality assessment of chest CT scans used in functional respiratory imaging [12032-68] 20 Iterative material ...
to volumetric images for image-guided proton therapy PART TWO 1V Perceptually improved T1-T2 MRI translations using conditional generative adversarial networks [12032-64] 1W Reconstruction 1TCoarse-to-fine ...
doi:10.1117/12.2638192
fatcat:ikfgnjefaba2tpiamxoftyi6sa
Front Matter: Volume 11313
2020
Medical Imaging 2020: Image Processing
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The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon. ...
Please use the following format to cite material from these proceedings: Publication of record for individual papers is online in the SPIE Digital Library. ...
loss for GAN-based super-resolution of clinical CT images using micro CT image database 11313 07 GANet: group attention network for diabetic retinopathy image segmentation 11313 08 Fully automated segmentation ...
doi:10.1117/12.2570657
fatcat:be32besqknaybh6wibz7unuboa
Deep Learning in Medical Imaging
2019
Neurospine
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In this review article, we will explain the history, development, and applications in medical imaging. ...
However, due to the low computing power and insufficient learnable data, ANN has suffered from overfitting and vanishing gradient problems for training deep networks. ...
Image to Image Translation With Using GAN Isola et al. 86 used conditional GAN to perform image to image translation with pixel to pixel correspondence. This model is called a pix2pix network. ...
doi:10.14245/ns.1938396.198
pmid:31905454
pmcid:PMC6945006
fatcat:miszi3fiojh35ldsgggxgpaowa
Recent advances and clinical applications of deep learning in medical image analysis
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2105.13381v1 fatcat:yxa6asxvcra3bixp4habjdbcbaOther Versions
Especially, we emphasize the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in medical image analysis, which are summarized based on different application ...
Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging ...
DLIR has four stages to progressively perform image registration. ...
arXiv:2105.13381v2
fatcat:2k342a6rhjaavpoa2qoqxhg5rq
Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data
[article]
2019
arXiv
pre-print
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Furthermore, for the segmentation task, we propose a novel method, called Confidence-guided Brain Anatomy Segmentation (CBAS) network, where segmentation and corresponding confidence maps are estimated ...
We show that our method can synthesize high-quality US images for every manipulated segmentation label with qualitative and quantitative improvements over the recent state-of-the-art synthesis methods. ...
In order to achieve better performance in tasks like medical image segmentation, [16] , [17] , [18] modeled epistemic uncertainty for learning the CNN network weights. ...
arXiv:1912.08364v1
fatcat:f6ouubd7g5ebjj63n2y6fweylq
Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments
[article]
2020
arXiv
pre-print
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One of the most significant challenges in statistical signal processing and machine learning is how to obtain a generative model that can produce samples of large-scale data distribution, such as images ...
Generative Adversarial Network (GAN) is an effective method to address this problem. The GANs provide an appropriate way to learn deep representations without widespread use of labeled training data. ...
Nowadays, GANs are widely used in various examples, such as text-to-image synthesis, image-to-image translation, and many potential medical applications. ...
arXiv:2005.13178v1
fatcat:u57mmd76njef7nclxgeyzycziy
A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
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3D Imaging Data by Visualizations 693 A Cascaded Refinement GAN for Phase Contrast Microscopy Image Super Resolution 702 DeepDRR -A Catalyst for Machine Learning in Fluoroscopy-guided Procedures 705 Evaluating ...
491 An Open Framework Enabling Electromagnetic Tracking in Image-Guided Interventions 492 Small Lesion Classication in Dynamic Contrast Enhancement MRI for Breast Cancer Early Detection 494 Uncertainty ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
Deep learning in medical image registration
2020
Progress in Biomedical Engineering
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of unmet clinical needs and potential directions for future research in deep learning-based medical image registration. ...
With the advent of deep learning, there have been significant advances in algorithmic performance for various computer vision tasks in recent years, including medical image registration. ...
In numerous multi-modal registration approaches, GAN-based image translation networks (e.g. ...
doi:10.1088/2516-1091/abd37c
fatcat:74w7ra4f7nfrrpfk2ifvmijntq
Generative Adversarial Networks Applied to Observational Health Data
[article]
2020
arXiv
pre-print
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We conducted a review of GAN algorithms for OHD in the published literature, and report our findings here. ...
Generative Adversarial Networks (GAN) have Generative Adversarial Networks (GAN) have recently emerged as a groundbreaking approach to efficiently learn generative models that produce realistic Synthetic ...
Primarily, algorithms developed for images and text in other fields were easily repurposed for medical equivalents. ...
arXiv:2005.13510v1
fatcat:v6ukcv7td5gddalp4svwa2z2di
The promise of artificial intelligence and deep learning in PET and SPECT imaging
2021
Physica medica (Testo stampato)
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Finally, the challenges, opportunities, and barriers to full-scale validation and adoption of AI-based solutions for improvement of image quality and quantitative accuracy of PET and SPECT images in the ...
A brief description of deep learning algorithms and the fundamental architectures used for these applications is also provided. ...
For instance, deep learning-guided CT image reconstruction developed by GE Medical Systems obtained FDA approval [30] . ...
doi:10.1016/j.ejmp.2021.03.008
pmid:33765602
fatcat:onw4fm22y5cxndxiwyy5bdw4t4
Generative Adversarial Network Technologies and Applications in Computer Vision
2020
Computational Intelligence and Neuroscience
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The emergence of generative adversarial networks (GANs) provides a new method and model for computer vision. ...
The latest research progress of GANs in artificial intelligence (AI) based security attack and defense is introduced. ...
task, it
can generate images different from the
input image guide
Computational Intelligence and Neuroscience
Table 4 : 4 Comparisons of GAN models on the structure.Figure 3: MAD-GAN model structure ...
doi:10.1155/2020/1459107
pmid:32802024
pmcid:PMC7416236
fatcat:bwqbonr34nfb7mw5omomzmamna
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
[article]
2020
arXiv
pre-print
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Thirdly, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, medical field, and data science are illustrated. ...
Finally, the future open research problems for GANs are pointed out. ...
ACKNOWLEDGMENTS The authors would like to thank the NetEase course taught by Shuang Yang, Ian Good fellow's invited talk at AAAI 19, CVPR 2018 tutorial on GANs, Sebastian Nowozin's MLSS 2018 GAN lecture ...
arXiv:2001.06937v1
fatcat:4iqb2vnhezgjnphfv3taej7vbu
A Generative Model for Volume Rendering
2018
IEEE Transactions on Visualization and Computer Graphics
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We show how to guide the user in transfer function editing by quantifying expected change in the output image. ...
We use the Generative Adversarial Network (GAN) framework to compute a model from a large collection of volume renderings, conditioned on (1) viewpoint and (2) transfer functions for opacity and color. ...
ACKNOWLEDGEMENTS We thank Peer-Timo Bremer for stimulating discussions. This work was partially supported by the National Science Foundation IIS-1654221. ...
doi:10.1109/tvcg.2018.2816059
pmid:29993811
fatcat:jqfaopyry5c6pj64ouvuc4hgm4
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