Generative adversarial network in medical imaging: A review.
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TLDR
A review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.About:
This article is published in Medical Image Analysis.The article was published on 2019-12-01 and is currently open access. It has received 1053 citations till now. The article focuses on the topics: Generative model.read more
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A survey on Image Data Augmentation for Deep Learning
TL;DR: This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing DataAugmentation, a data-space solution to the problem of limited data.
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Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
TL;DR: This article provides a detailed review of the solutions above, summarizing both the technical novelties and empirical results, and compares the benefits and requirements of the surveyed methodologies and provides recommended solutions.
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U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications
TL;DR: A narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends, and discusses the many innovations that have advanced in deep learning and how these tools facilitate U-nets.
Journal ArticleDOI
MedGAN: Medical image translation using GANs
Karim Armanious,Karim Armanious,Chenming Jiang,Marc Fischer,Thomas Küstner,Tobias Hepp,Konstantin Nikolaou,Sergios Gatidis,Bin Yang +8 more
TL;DR: A new framework, named MedGAN, is proposed for medical image-to-image translation which operates on the image level in an end- to-end manner and outperforms other existing translation approaches.
Journal ArticleDOI
Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment
Mohammad Jamshidi,Ali Lalbakhsh,Jakub Talla,Zdenek Peroutka,Farimah Hadjilooei,Pedram Lalbakhsh,Morteza Jamshidi,Luigi La Spada,Mirhamed Mirmozafari,Mojgan Dehghani,Asal Sabet,Saeed Roshani,Sobhan Roshani,Nima Bayat-Makou,Bahare Mohamadzade,Zahra Malek,Alireza Jamshidi,Sarah Kiani,Hamed Hashemi-Dezaki,Wahab Mohyuddin +19 more
TL;DR: A response to combat the virus through Artificial Intelligence (AI) is rendered in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers.
References
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the DIARETDB1 diabetic retinopathy database and evaluation protocol
Tomi Kauppi,V. Kalesnykiene,Joni-Kristian Kamarainen,Lasse Lensu,Iiris Sorri,A. Raninen,Raimo Voutilainen,Hannu Uusitalo,Heikki Kälviäinen,Juhani Pietilä +9 more
TL;DR: With the proposed database and protocol, it is possible to compare different algorithms, and correspondingly, analyse their maturity for technology transfer from the research laboratories to the medical practice.
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Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
Qingsong Yang,Pingkun Yan,Yanbo Zhang,Hengyong Yu,Yongyi Shi,Xuanqin Mou,Mannudeep K. Kalra,Ge Wang +7 more
TL;DR: This paper introduces a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity that is capable of not only reducing the image noise level but also trying to keep the critical information at the same time.
Posted Content
Adversarially Learned Inference
Vincent Dumoulin,Ishmael Belghazi,Ben Poole,Olivier Mastropietro,Alex Lamb,Martin Arjovsky,Aaron Courville +6 more
TL;DR: The adversarially learned inference (ALI) model is introduced, which jointly learns a generation network and an inference network using an adversarial process and the usefulness of the learned representations is confirmed by obtaining a performance competitive with state-of-the-art on the semi-supervised SVHN and CIFAR10 tasks.
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INbreast: toward a full-field digital mammographic database.
TL;DR: A new mammographic database built with full-field digital mammograms, which presents a wide variability of cases, and is made publicly available together with precise annotations is presented and can be a reference for future works centered or related to breast cancer imaging.
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Current Status of the Digital Database for Screening Mammography
Michael D. Heath,Kevin W. Bowyer,Daniel B. Kopans,W. Philip Kegelmeyer,Richard H. Moore,K.I. Chang,S. Munishkumaran +6 more
TL;DR: The Digital Database for Screening Mammography is a resource for use by researchers investigating mammogram image analysis, focused on the context of image analysis to aid in screening for breast cancer.