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Uncertainty-Guided Progressive GANs for Medical Image Translation [article]

Uddeshya Upadhyay, Yanbei Chen, Tobias Hepp, Sergios Gatidis, Zeynep Akata
2021 arXiv   pre-print
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]

Richard Osuala, Kaisar Kushibar, Lidia Garrucho, Akis Linardos, Zuzanna Szafranowska, Stefan Klein, Ben Glocker, Oliver Diaz, Karim Lekadir
2021 arXiv   pre-print
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

Ivana Išgum, Olivier Colliot
2022 Medical Imaging 2022: Image Processing  
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

Bennett A. Landman, Ivana Išgum
2020 Medical Imaging 2020: Image Processing  
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

Mingyu Kim, Jihye Yun, Yongwon Cho, Keewon Shin, Ryoungwoo Jang, Hyun-jin Bae, Namkug Kim
2019 Neurospine  
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]

Xuxin Chen, Ximin Wang, Ke Zhang, Roy Zhang, Kar-Ming Fung, Theresa C. Thai, Kathleen Moore, Robert S. Mannel, Hong Liu, Bin Zheng, Yuchen Qiu
2021 arXiv   pre-print
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]

Jeya Maria Jose V., Rajeev Yasarla, Puyang Wang, Ilker Hacihaliloglu, Vishal M. Patel
2019 arXiv   pre-print
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]

Pegah Salehi, Abdolah Chalechale, Maryam Taghizadeh
2020 arXiv   pre-print
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]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
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

Xiang Chen, Andres Diaz-Pinto, Nishant Ravikumar, Alejandro Frangi
2020 Progress in Biomedical Engineering  
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]

Georges-Filteau, Jeremy, Cirillo Elisa
2020 arXiv   pre-print
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

Hossein Arabi, Azadeh AkhavanAllaf, Amirhossein Sanaat, Isaac Shiri, Habib Zaidi
2021 Physica medica (Testo stampato)  
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

Lianchao Jin, Fuxiao Tan, Shengming Jiang
2020 Computational Intelligence and Neuroscience  
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]

Jie Gui, Zhenan Sun, Yonggang Wen, Dacheng Tao, Jieping Ye
2020 arXiv   pre-print
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

Matthew Berger, Jixian Li, Joshua Aaron Levine
2018 IEEE Transactions on Visualization and Computer Graphics  
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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