/F2 9 Tf Q For A, only uni- (P=0.03), but not normal or bilateral defect scans (P0.08) reached significance when compared to real images. They also processed data and conducted analysis. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in 105.816 18.547 l /F2 276 0 R /R8 53 0 R Applications of artificial intelligence in nuclear medicine image generation. Zhu, J-Y., Krhenbhl, P., Shechtman, E., Efros, A.A. Generative Visual Manipulation on the Natural Image Manifold2016 September 01, 2016: [arXiv:1609.03552 p.]. 3479.84 4463.01 l Machine Learning, Computer Vision, Computer and Information Systems Applications, Over 10 million scientific documents at your fingertips, Not logged in /R10 9.9626 Tf /R58 66 0 R /R40 33 0 R 34(7), 512 . This layer also adjusted the data dimension so that the input block is acceptable. h Experimental Quantum Generative Adversarial Networks for Image Generation /R22 27 0 R >> By editing that latent vector, variations of the desired image are generated. << /R123 160 0 R q The authors narrow this knowledge gap by designing a flexible quantum GAN scheme, and realizing this scheme on . /R38 56 0 R /Annots [ ] Erickson, B. J. Generative adversarial network based on semantic consistency for text Guidelines and recommendations for perfusion imaging in cerebral ischemia: A scientific statement for healthcare professionals by the writing group on perfusion imaging, from the council on cardiovascular radiology of the American heart association. /ca 1 Takahiro Higuchi. 109.984 9.465 l 2014 June 01, 2014. https://ui.adsabs.harvard.edu/#abs/2014arXiv1406.2661G. 17.5859 -13.948 Td 3). 3989.85 3895.02 l CAS /Length 1949 /R26 7.53477 Tf His research interests are in the areas of computer vision and deep learning, especially generative adversarial networks and unsupervised learning. endobj /R10 9.9626 Tf BT /MediaBox [ 0 0 612 792 ] Med. Q q 4286.34 4794.21 l 4764.68 4862.48 m /R26 9.9322 Tf Q /Filter /DCTDecode ET ET %&'()*456789:CDEFGHIJSTUVWXYZcdefghijstuvwxyz /R186 204 0 R 1.009 0 0 1 540.606 81 Tm [ (\056) 68.9919 (\224) ] TJ /R10 35 0 R [ (\135) -279.008 (\050e\056g\056) -407.987 (\223Dra) 14.9869 (w) -278.986 (a) -279.985 (coat) -279.005 (with) ] TJ /R120 163 0 R Ann. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 9(9), 821 (2021). A review on AI in PET imaging. f* PubMed Central Discriminator in our model. /a0 << 211.378 0 Td q /R205 154 0 R /Resources << To address this issue, a light-weight GAN (FastGAN) has been recently proposed to enable learning with a smaller set of supervised real data, thereby allowing to reduce the number of initially provided items serving as stimuli21. /Resources << 933--41. Sci Rep 12, 18787 (2022). /R14 9.9626 Tf https://ui.adsabs.harvard.edu/abs/2014arXiv1411.1784M. -0.00904 Tc Ann. q Ann. Generative Adversarial Networks for Astronomical Images Generation [ (pro) 15.0135 (vides) -199.019 (a) -198.989 (relati) 25.0058 (v) 15.006 (e) -198.989 (constraint) -199.006 (in) -199.987 (the) -199.011 (form) -199.021 (of) -200.002 (tw) 11.0076 (o) -200.002 (images) -198.986 (mean\055) ] TJ Q /Parent 1 0 R /R116 232 0 R n >> [ (our) -216.014 (e) 20.9811 (xperiments\054) -224.013 (we) -215.989 (show) -216.01 (that) -215.013 (our) -216.014 (GAN) -215.998 (fr) 15.0085 (ame) 16.0162 (work) -215.998 (is) -216.005 (able) -216.013 (to) ] TJ Applying blood flow 123I-IMP SPECTs to our novel modified FastGAN, created scans were indistinguishable to acquired images from real patients, including normal studies and various degrees of ischemia. Both the input and up-sampling blocks enlarged the feature maps progressively to produce more detailed image, while the output block generated a monochromatic brain SPECT image from the input feature maps. /F2 39 0 R >> Xia, T. et al. 4099.7 5241.23 l S -0.07048 Tc Q 6 0 obj endobj 7 0 obj /R19 15 0 R 3985.84 5248.38 l https://ui.adsabs.harvard.edu/abs/2017arXiv171010196K. 19.9563 0 Td 4303.56 5048.55 l Ann. 0 g Q /MediaBox [ 0 0 612 792 ] -148.719 -11.9551 Td BT 0.44706 0.57647 0.77255 rg https://doi.org/10.1038/s41598-022-23325-3, DOI: https://doi.org/10.1038/s41598-022-23325-3. Before GAN was proposed, researchers' research on deep learning focused on deep discriminative models. q 6, 97080, Wrzburg, Germany, Rudolf A. Werner,Takahiro Higuchi&Yohji Matsusaka, The Russell H Morgan Department of Radiology and Radiological Sciences, Division of Nuclear Medicine and Molecular Imaging, Johns Hopkins School of Medicine, Baltimore, MD, USA, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan, Department of Systems Innovation, Graduate School of Engineering, The University of Tokyo, Bunkyo-Ku, Japan, Department of Nuclear Medicine, Saitama Medical University International Medical Center, Saitama, Japan, Department of Systems and Informatics, Hokkaido Information University, Ebetsu, Japan, You can also search for this author in /R191 217 0 R For instance, generative adversarial networks (Goodfellow et al., 2014) are able to produce realistic images with state of the art quality (Karras et al., 2020). 10 0 0 10 0 0 cm /R37 25 0 R 10 0 0 10 0 0 cm AI approach of cycle-consistent generative adversarial networks to synthesize PET images to train computer-aided diagnosis algorithm for dementia. 77.262 5.789 m 3981.84 3887.93 l generative models tutorialhierarchically pronunciation google translate. /R91 115 0 R [ 42.8112 32.1084 ] 0 d 0.01295 Tc /Annots [ ] /R136 183 0 R 1 0 0 1 532.494 324.747 Tm BT /x6 Do q /R26 7.53477 Tf 0.98 0 0 1 50.1121 140.776 Tm In: Doina P, Yee Whye T, editors. Narrative review of generative adversarial networks in medical and molecular imaging. /R10 9.9626 Tf In recent years, the use of artificial intelligence (AI) based on neural networks in medical imaging has been rapidly expanding. /R183 199 0 R Deep convolutional generative adversarial networks (GAN) allow for creating images from existing databases. 4835.79 4274.32 l /R151 231 0 R /R117 233 0 R 5206.08 4508.36 l 155.833 0 0 147.271 5135.54 4179.36 cm T* 1 0 0 1 422.131 388.995 Tm GANs, Generative Adversarial Networks [17] which are conditioned on textual descriptions, are capable of generating images that are very realistic and can fool the mind into believing that these images are genuine. 1 0 0 1 245.65 176.641 Tm BT 1.02 0 0 1 49.5539 384.955 Tm S >> Of note, all of these studies enrolled a sufficiently large number of patients, while for other brain disorders, adequate patient recruitment may be challenging, e.g., to detect left or right hemispheric abnormalities in patients affected with Creutzfeldt-Jakob disease using 123I-IMP30. 10 0 0 10 0 0 cm The generator and discriminator were trained alternately in the following steps: (i) Synthesized images were outputted by the generator. 10.7028 w 0 1 0 rg /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] BT (\054) Tj /R10 9.9626 Tf /ProcSet [ /Text /ImageC /ImageB /PDF /ImageI ] /R209 249 0 R >> (PDF) Generative Adversarial Networks - ResearchGate I.K. However, most current methods only allow for users to guide this image generation process through limited interactions. << & Zhang, Y. GAN-based synthetic brain PET image generation. /ColorSpace << /R177 192 0 R q 0.98 0 0 1 280.236 420.821 Tm q 1.017 0 0 1 308.862 324.747 Tm 5271.4 4550.33 l 4829.88 4347.89 l ET f For instance, representing diversity for each defect pattern, the radiotracer accumulation in pixel-wise SD maps of bilateral ischemia generated by dataset A were lower than the real images, in particular for the frontal and occipital lobe (Fig. /Parent 1 0 R /Annots [ ] Nucl. 10 0 0 10 0 0 cm >> Generative Adversarial Networks. 11.9551 TL Med Image Anal. Neurol. S & Hawkins, C. M. The role of generative adversarial networks in radiation reduction and artifact correction in medical imaging. /CA 0.5 467.498 0 0 467.498 3150.81 4443.07 cm [ (than) -246.011 (image) ] TJ As such, data augmentation based on processing techniques provides images similar to supervised data, e.g., by applying geometric deformation, brightness, saturation changes, random cropping, or mix-ins to natural images3. Google Scholar. We developed a light-weight GAN model for brain SPECT imaging that allowed us to create normal scans, but also varying degrees of cerebral ischemia closely resembling realistic images. 4764.68 4436.11 m /Rotate 0 GLU is a gating unit proposed in42. >> PubMed Central /Pages 1 0 R Xudong Mao, 1 0 0 1 416.245 464 Tm /R50 82 0 R Q This technology is based on a neuronal network using both real images from actual patients fed to the GAN, a generator (trying to provide real images) and a discriminator (verifying whether the created scan is real or an imitation)31. -235.441 -11.9563 Td J. R. Soc. Kwon, G., Han, C., Kim, D-s. Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks. /R193 215 0 R 3287.5 4972.71 m [ (in) -315.013 (comple) 15 (x) -316.011 (data) -314.985 (domains) -314.996 (\133) ] TJ [ (propose) -252.99 (a) -251.991 (no) 14.9829 (v) 15.0101 (el) -252.016 (GAN) -253.017 (technique) -251.986 (we) -253.005 (call) -252.99 (CONst) 0.99408 (rained) -253 (GAN) ] TJ Download PDF Abstract: We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D . /R10 35 0 R /R182 200 0 R f (2, 3, 4), respectively. (1). 6). [ (ing) -245.99 (\223Generate) -246.011 (an) -246 (image) -246.009 (more) -246.002 (lik) 10 (e) -245.997 (image) ] TJ Q Yordanova, A. et al. First row: mean counts, second row: left to right hemisphere ratio (LR). 1.018 0 0 1 308.862 190.941 Tm /R84 134 0 R Generative Adversarial Networks for Image Generation, https://doi.org/10.1007/978-981-33-6048-8, 12 b/w illustrations, 29 illustrations in colour, Shipping restrictions may apply, check to see if you are impacted, Computer and Information Systems Applications, Tax calculation will be finalised during checkout. 0 1 0 rg 10 0 0 10 0 0 cm Generative Adversarial Networks for Image Generation Authors: Xudong Mao, Qing Li Offers an overview of the theoretical concepts and the current challenges of generative adversarial networks Proposes advanced GAN image generation approaches with higher image quality and better training stability /R16 9.9626 Tf -57.5383 76.6969 Td S Q /R157 225 0 R (a) Medical images related to the tissue geometry (here PAT co-registered to ultrasound (US) data) are semantically segmented. This book appeals to students and researchers who are interested in GANs, image generation and general machine learning and computer vision. -130.263 -11.9559 Td (4). Whether quantum generative adversarial networks (quantum GANs) implemented on near-term devices can actually solve real-world learning tasks, however, has remained unclear. /R23 26 0 R 0 0 0 SCN Generative Adversarial Networks for Image Generation The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. /F1 254 0 R [ (\050e) 14.9826 (\056g) 14.9948 (\056) -307.997 (classes\054) -248.018 (attrib) 20.0175 (utes\054) -246.99 (object) -247.006 (r) 37.0163 (elationships\054) -248.013 (color) 110.982 (\054) -247.014 (etc\056\051\056) -308.985 (In) ] TJ 8 M https://ui.adsabs.harvard.edu/abs/2019arXiv190802498K. [ (A\073) -167.781 (B) -0.49992 ] TJ /R56 79 0 R /ExtGState << Koshino, K. et al. 0.991 0 0 1 308.862 140.776 Tm These results suggest potential use of generative models in the training of DL networks and as a means of data sharing across institutions without patient information confidentiality issues. f /R141 210 0 R f >> /R38 56 0 R q 4835.79 4521.26 m /a0 gs /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] \(x\) and \(\widehat{x}\) were sampled from real images \({I}_{real}\) and the generated images \(G\left(z|y\right)\), respectively. 4101.93 3896.84 l /Font << >> GAN uses a simple training strategy of competing generator and discriminator against each other to synthesize images closely resembling real ones. Q The feature map and the global feature map from the third down-sampling block were input to the simple decoders to reconstruct the regional and whole real image from these feature maps. << /R86 123 0 R 4). [ ] 0 d The authors declare no competing interests. h [ (unsupervised) -244.992 (GANs) -245.009 (while) -246.016 (satisfying) -244.981 (a) -245.013 (lar) 36.009 (g) 10.0017 (e) -245.004 (number) -244.987 (of) -246.011 (the) ] TJ Google Scholar. [ (than) -232.989 (ima) 10.0123 (g) 11.0051 (e) ] TJ /R128 165 0 R /R33 Do S /R8 53 0 R 2 0 obj Open Access funding enabled and organized by Projekt DEAL. /R10 9.9626 Tf Q Expand 74 PDF End-to-End Adversarial Retinal Image Synthesis /R201 150 0 R /R19 cs [ (the) -249.99 (model) -250.012 (to) -249.985 (produce) -249.99 (acceptable) -249.997 (images\056) ] TJ (i) The generative adversarial network (GAN)-based generation of anatomical parameter images. 1 j Author: Xudong Mao Publisher: Springer Nature ISBN: 9813360488 Category : Computers Languages : en Pages : 77 Get Book. The generative adversarial network for text-to-image generation is proposed, which integrates the text-to-image generation module and the semantic comparison module into a framework. Currently he is the Chairman of the Hong Kong Web Society, a councillor of the Database Society of Chinese Computer Federation (CCF), a member of the CCF Big Data Experts Committee, and a member of the international WISE Societys steering committee. Although dataset A using CER, BG, and COR as input provided more realistic images than B (only utilizing COR), we only applied a maximum of three anatomical compartments to create images that are indistinguishable to their real equivalents of patients (Fig. A novel approach was recently proposed using 123I-ioflupane SPECT, which aimed to mimic characteristics of Parkinson's disease by integrating a transformer-based technique, which is based on a framework different from GAN41. J. Nucl. P.O. Book Description Generative adversarial networks (GANs) were introduced by Ian Goodfellow and his co-authors including Yoshua Bengio in 2014, and were to referred by Yann Lecun (Facebook's AI research director) as "the most interesting idea in the last 10 years in ML." /Rotate 0 10 0 0 10 0 0 cm 0.999 0 0 1 417.316 104.91 Tm >> [ (B) -0.49992 ] TJ /R16 48 0 R 1 0 0 1 518.572 253.016 Tm /R58 66 0 R /R56 79 0 R In this regard, both the generator and discriminator develop consecutively, e.g., by adding more and more details during the training process, ultimately leading to further stabilization of the produced scans32. /R185 201 0 R 8 M We refer to these approaches here as direct image generation. Article >> >> 0 j S stream [ (Co) -6.01502 (n) -5.99465 (s) -5.00347 (t) -4.98989 (r) -12.0097 (a) -12.0233 (i) -4.98989 (n) -5.99465 (t) ] TJ Generative stochastic networks [4] are an example of a generative machine that can be trained with exact backpropagation rather than the numerous ap-proximations required for Boltzmann machines. >> /ExtGState << >> Dr. Li has published over 400 papers in technical journals and international conferences in these areas, and is actively involved in the research community by serving as a journal reviewer, program committee chair/co-chair, and as an organizer/co-organizer of numerous international conferences. (10753) Tj 0.43921 0.67773 0.27832 SCN /R137 184 0 R 2022 Springer Nature Switzerland AG. Get the most important science stories of the day, free in your inbox. [ (constr) 14.9857 (aints) -254.018 (pr) 44.0046 (o) 10 (vided) -254.011 (by) -253.993 (user) 9.98248 (s\054) -256.007 (ef) 18.0033 (fectively) -253.985 (c) 15.0061 (hanging) -254.014 (a) -254.007 (GAN) ] TJ [ (the) -255.993 (constraints) -257.007 (with) -255.993 (respect) -255.989 (to) -256.008 (a) -256.998 (mapping) -256.003 (to) -256.008 (an) -256.993 (underlying) ] TJ 0 g Imaging https://doi.org/10.1007/s00259-022-05805-w (2022). S 0.98 0 0 1 308.862 336.702 Tm J. /R28 23 0 R For specifying the pattern of radiotracer accumulation, a conditional image was applied, in which number of channels were 6 and 3 for the dataset A and B, respectively. >> Nucl. /R50 82 0 R /Resources << endobj 3206.75 3769.51 2126.29 1733.85 re Yamauchi, M. et al. Total loss of the discriminator \({\mathcal{L}}_{D}\) was given by: Each slice was normalized by the maximum count of the slice. 4(4), 159163 (2018). Q /R50 82 0 R 2017 October 01, 2017: [arXiv:1710.10196 p]. Q f f Kim, K. et al. 3492.2 4416.77 m 12 0 obj [ (form) -233.015 (\223Gener) 15.011 (ate) -233.019 (an) -232.008 (ima) 10.0123 (g) 10.0061 (e) -232.998 (mor) 37.9926 (e) -232.998 (lik) 10.0086 (e) -232.998 (ima) 10.0123 (g) 11.0051 (e) ] TJ High-quality face image generation based on generative adversarial networks 67.215 22.738 71.715 27.625 77.262 27.625 c Quantum machine learning is expected to be among the first practical applications of near-term quantum devices. T* In this regard, GAN is a promising technology for medical imaging, and has been actively studied for various purposes such as data augmentation, modality conversion, segmentation, super-resolution, denoising and reduction of radiation exposure for medical imaging4,6,7,8,9,10,11. (30) Tj /F1 125 0 R /R52 86 0 R 1.02 0 0 1 50.1121 152.731 Tm 4284.95 4390.7 l In order to achieve stability of network, we replace MLP with convolutional neural network (CNN) and remove pooling layers. ET /R7 34 0 R 0.98 0 0 1 50.1121 420.821 Tm 1 0 0 1 429.256 492.256 Tm S n [ (semantic) -246.011 (space\056) -306.989 (The) -247.002 (generator) -246.002 <7361746973026573> -247.009 (a) -245.997 (constraint) ] TJ q /ExtGState << best place to buy rubber hex dumbbells Latest News News generative adversarial networks /R29 Do /R48 70 0 R 0.999 0 0 1 308.862 104.91 Tm /R10 9.9626 Tf /R38 9.9626 Tf Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., et al. The number of selected slices for defect patterns and slice levels are shown in Table 1. /Resources << Q This indicates that a lack of diversity within a specific pattern of cerebral ischemia may also lead to less realistic images. Interface 15(141), 20170387 (2018). DOI: 10.1103/PhysRevApplied.16.024051 Corpus ID: 222310667; Experimental Quantum Generative Adversarial Networks for Image Generation @article{Huang2021ExperimentalQG, title={Experimental Quantum Generative Adversarial Networks for Image Generation}, author={He-Liang Huang and Yuxuan Du and Ming Gong and You-Wei Zhao and Yulin Wu and Chaoyue Wang and Shaowei Li and Futian Liang and Jin Lin and . Med. 4329.15 4794.21 m The value of \(t\) was a random integer from -2 to 2. /Width 91 were involved in data creation. T* 10.5469 0 Td /Parent 1 0 R /R58 66 0 R /Subject (IEEE Conference on Computer Vision and Pattern Recognition) 4 0 obj >> 11.9551 TL [ (into) -334.996 (one) -335.019 (that) -334.996 (allows) -334.99 (user) 8.98303 (s) -334.985 (inter) 14.9953 (active) -334.998 (contr) 43.9854 (ol) -335 (o) 10.0017 (ver) -334.996 (ima) 10.0137 (g) 9.00225 (e) ] TJ PubMed As such, if reasonable amounts of supervised stimuli are provided, the applied FastGAN algorithm may allow to yield sufficient number of molecular brain scans for various clinical scenarios, e.g., for imbalanced datasets in the context of orphan diseases or data-hungry deep learning technologies. Iida, H. et al. Q /R19 cs 4814.16 4315.42 l 4103.82 4486.07 414.246 267.266 re 4302.39 5152.64 l 1.009 0 0 1 308.862 81 Tm /R14 44 0 R /R19 CS 4721.96 4640.23 m 5007.39 4381.62 l >> /R206 151 0 R CAS /R197 219 0 R Created scans were then validated by quantitative comparison with images of real patients, which allowed to determine whether FastGAN-based scans of reduced cerebral blood flow resemble their real equivalents. [ (\051) -0.90181 ] TJ 14.4 TL /R65 102 0 R Clin. 0.98 0 0 1 308.503 92.9551 Tm /R10 9.9626 Tf Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 2019 August 01, 2019. https://ui.adsabs.harvard.edu/abs/2019arXiv190810468B. 3206.75 3769.51 2126.29 1733.85 re (\053) Tj 3206.75 3769.51 2126.29 1733.85 re /MediaBox [ 0 0 612 792 ] Symbols F, s, p and a denote channels of output feature maps, strides, padding and slope of Leaky ReLU activation function, respectively. 1.02 0 0 1 186.1 200.552 Tm /R7 34 0 R /Type /Page Adobe d C 10 0 0 10 0 0 cm /R173 196 0 R In this regard, a latent vector serving as input source of the desired image is searched. Briefly, regional feature maps with half height and half width were cropped at a random location of the feature map from the second down-sampling block. q q ET 4287.43 5055.51 l /a1 gs (1) Tj 2014 November 01, 2014:[arXiv:1411.784 p.]. You will also use a variety of datasets for the different projects covered in the book. [ (th) -3.02617 (a) ] TJ For our network model, we applied the previously published FastGAN21 to conditional GAN19 with modification for specifying defect pattern and adaptation to image matrix size. >> 10 0 0 10 0 0 cm This work was supported partially by Grant-in-Aid for Scientific Research (Grant Number 22H03027) of Japan Society for the Promotion of Science (JSPS) and was also supported by the RECTOR program at Okayama (TH). 0.999 0 0 1 424.791 104.91 Tm 100.875 27.707 l 3492.6 5200.34 l Generative Adversarial Networks. 78.852 27.625 80.355 27.223 81.691 26.508 c 3206.75 3769.51 2126.29 1733.85 re 3479.84 4676.85 m 5138.52 4757.39 m /R16 9.9626 Tf /R81 116 0 R [ (1\056) -249.99 (Intr) 18.0146 (oduction) ] TJ Symbols H, W and F in feature maps denote height, width and channels, respectively. BT /R10 9.9626 Tf AI approach of cycle-consistent generative adversarial networks to synthesize PET images to train computer-aided diagnosis algorithm for dementia. /Annots [ ] The epochs, where highest accuracy for real images were given, were determined by a board-certified nuclear medicine physician with 10years of experience (T.H.) 0 0 0 scn n 4056.89 5219.82 l /R19 cs (15) Tj /R10 35 0 R 10 0 0 10 0 0 cm 5007.39 4760.35 m /R139 186 0 R /Resources << If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Book Title: Generative Adversarial Networks for Image Generation, DOI: https://doi.org/10.1007/978-981-33-6048-8, eBook Packages: This project was also partially supported by the German Research Foundation (DFG, 453989101, TH, RAW; 507803309, RAW). /R225 264 0 R 0 0 0 scn /ColorSpace << To obtain /R19 cs /R31 31 0 R -0.0748 Tc /R8 53 0 R endobj Images were performed under rest and stress condition at one day and thus, a total of 500 scans were available for analyses. 1.009 0 0 1 485.712 81 Tm Shorten, C. & Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. Significant progress has been made by the advances in Generative Adversarial Networks (GANs) for image generation. /R230 267 0 R /F1 266 0 R For instance, in patients with cerebral ischemia using N-isopropyl p-I-123-iodoamphetamine(123I-IMP) SPECT, various defect patterns can be recorded, e.g., affecting only one hemisphere or global reduced blood flow20. Commun. h [ (B) -0.49992 ] TJ Although image generation has been challenging, GAN image generation has proved to be very successful and impressive. /R8 53 0 R -49.7027 -13.9469 Td 10 0 0 10 0 0 cm /R26 7.19228 Tf /R87 122 0 R /a1 << /R14 9.9626 Tf As such, we aimed to reduce the number of needed real input images by applying only one skip-layer excitation, as such an approach allowed for minimizing the number of parameters needed to be learned along with a small matrix size of input images. Latchaw, R. E. et al. [PDF] Dual Projection Generative Adversarial Networks for Conditional /F2 124 0 R Since Goodfellow et al. This work proposes a novel method based on Generative Adversarial Networks (GANs) to train a segmentation model with both labeled and unlabeled images, which prevents over-fitting by learning to discriminate between true and fake patches obtained by a generator network. Evaluation of Generative Adversarial Networks for High-Resolution
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