Understand
The availability of large-scale chest X-ray datasets is a requirement for developing well-performing deep learning-based algorithms in thoracic abnormality detection and classification.
- However, biometric identifiers in chest radiographs hinder the public sharing of such data for research purposes due to the risk of patient re-identification.
- To counteract this issue, synthetic data generation offers a solution for anonymizing medical images.
- This work employs a latent diffusion model to synthesize an anonymous chest X-ray dataset of high-quality class-conditional images.
Built on
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Diederik P Kingma and Jimmy Ba, · 2014
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Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli, · 2015
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“CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning,”
Pranav Rajpurkar et al., · 2017
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“Densely Connected Convolutional Networks,”
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger, · 2017
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“Learning to Recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks,”
Sebastian Gündel, Sasa Grbic, Bogdan Georgescu, Siqi Liu, Andreas Maier, and Dorin Comaniciu, · 2018
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“Progressive Growing of GANs for Improved Quality, Stability, and Variation,”
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen, · 2018
Earlier work this paper cites.
Similar
“cGANs with Projection Discriminator,”
Takeru Miyato and Masanori Koyama, · 2018
Cited alongside, same era.
“The Unreasonable Effectiveness of Deep Features as a Perceptual Metric,”
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang, · 2018
Cited alongside, same era.
“A gentle introduction to deep learning in medical image processing,”
Andreas Maier, Christopher Syben, Tobias Lasser, and Christian Riess, · 2019
Cited alongside, same era.
“Breaking medical data sharing boundaries by using synthesized radiographs,”
Tianyu Han et al., · 2020
Cited alongside, same era.
“Towards Improving Privacy of Synthetic DataSets,”
Aditya Kuppa, Lamine Aouad, and Nhien-An Le-Khac, · 2021
Cited alongside, same era.
Then
“Taming Transformers for High-Resolution Image Synthesis,”
Patrick Esser, Robin Rombach, and Björn Ommer, · 2021
Later among the works it cites.
“Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data,”
Kai Packhäuser, Sebastian Gündel, Nicolas Münster, Christopher Syben, Vincent Christlein, and Andreas Maier, · 2022
Closest in time.
“Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains,”
Pierre Chambon, Christian Bluethgen, Curtis P Langlotz, and Akshay Chaudhari, · 2022
Closest in time.
“High-Resolution Image Synthesis with Latent Diffusion Models,”
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer, · 2022
Closest in time.
“ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases,”
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers, · 2097
Closest in time.
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