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The success of Deep Learning applications critically depends on the quality and scale of the underlying training data.
Multiscale structural similarity for image quality assessment
Z. Wang, E.P. Simoncelli, and A.C. Bovik · 2003
Earlier work this paper cites.
Image Quality Assessment: From Error Visibility to Structural Similarity
Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli · 2003
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Generative Adversarial Networks, June 2014
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition, April 2015
Karen Simonyan and Andrew Zisserman · 2015
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How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?, January 2016
Junghwan Cho, Kyewook Lee, Ellie Shin, Garry Choy, and Synho Do · 2016
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Rethinking the Inception Architecture for Computer Vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Improved techniques for training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen, and Xi Chen · 2016
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GANs trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Image-to-Image Translation with Conditional Adversarial Networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros · 2017
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Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets
Ravi K. Samala, Heang-Ping Chan, Lubomir Hadjiiski, Mark A. Helvie, Caleb D. Richter, and Kenny H. Cha · 2018
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The Unreasonable Effectiveness of Deep Features as a Perceptual Metric, April 2018
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang · 2018
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High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro · 2018
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MedGAN: Medical image translation using GANs
Karim Armanious, Chenming Jiang, Marc Fischer, Thomas Küstner, Tobias Hepp, Konstantin Nikolaou, Sergios Gatidis, and Bin Yang · 2019
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CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison, January 2019
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, and Andrew Y. Ng · 2019
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Breaking medical data sharing boundaries by using synthesized radiographs
Diffusion Models Beat GANs on Image Synthesis, June 2021
Prafulla Dhariwal and Alex Nichol · 2021
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GAN-based Data Augmentation for Chest X-ray Classification, July 2021
Shobhita Sundaram and Neha Hulkund · 2021
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Alias-Free Generative Adversarial Networks, October 2021
Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2021
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Pros and Cons of GAN Evaluation Measures: New Developments, October 2021
Ali Borji · 2021
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High-Resolution Image Synthesis with Latent Diffusion Models, April 2022
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Brain Imaging Generation with Latent Diffusion Models
Walter H. L. Pinaya, Petru-Daniel Tudosiu, Jessica Dafflon, Pedro F. Da Costa, Virginia Fernandez, Parashkev Nachev, Sebastien Ourselin, and M. Jorge Cardoso · 2022
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Tianyu Han, Sven Nebelung, Christoph Haarburger, Nicolas Horst, Sebastian Reinartz, Dorit Merhof, Fabian Kiessling, Volkmar Schulz, and Daniel Truhn · 2020
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Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Histological image tiles for TCGA-CRC-DX, color-normalized, sorted by MSI status, train/test split, May 2020
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A review on medical imaging synthesis using deep learning and its clinical applications
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Deep learning detects genetic alterations in cancer histology generated by adversarial networks
Jeremias Krause, Heike I. Grabsch, Matthias Kloor, Michael Jendrusch, Amelie Echle, Roman David Buelow, Peter Boor, Tom Luedde, Titus J. Brinker, Christian Trautwein, Alexander T. Pearson, Philip Quirke, Josien Jenniskens, Kelly Offermans, Piet A. Brandt, and Jakob Nikolas Kather · 2021
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https://airogs.grand-challenge.org/
AIROGS - Grand Challenge
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Three-Dimensional Medical Image Synthesis with Denoising Diffusion Probabilistic Models
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