Diffusion Probabilistic Models for Synthetic Chest X-Ray Generation: A Framework for Data Augmentation in Rare Disease Detection with Preserved Pathological Lesions

cirpublications.com-dGD5-1790899041-v425916189 1790899041 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2024 3 1 Diffusion Probabilistic Models for Synthetic Chest X-Ray Generation: A Framework for Data Augmentation in Rare Disease Detection with Preserved Pathological Lesions George Papadopoulos Eleni Georgiou 2024 10.68159/v425916189 https://cirpublications.com/pub/journal/1/article/v425916189 Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, et al. CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In: Proc AAAI Conf Artif Intell. 2019;33(1):590-7. Johnson AEW, Pollard TJ, Greenbaum NR, Lungren MP, Deng CY, Peng Y, et al. MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs. arXiv [Preprint]. 2019:arXiv:1901.07042. Chen RJ, Lu MY, Chen TY, Williamson DFK, Mahmood F. Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng. 2021;5(6):493-7. 10.1038/s41551-021-00751-8 Schlegl T, Seeböck P, Waldstein SM, Langs G, Schmidt-Erfurth U. f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med Image Anal. 2019;54:30-44. 10.1016/j.media.2019.01.010 Baur C, Wiestler B, Albarqouni S, Navab N. Deep autoencoding models for unsupervised anomaly segmentation in brain MR images. In: Int MICCAI Brainlesion Workshop. Cham: Springer; 2018. p. 161-9. Sampath V, Maurtua I, Aguilar Martin JJ, Gutierrez A. A survey on generative adversarial networks for imbalance problems in computer vision tasks. J Big Data. 2021;8(1):27. 10.1186/s40537-021-00414-0 Baumgartner CF, Koch LM, Pollefeys M, Konukoglu E. An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation. In: Int Workshop Stat Atlases Comput Models Heart. Cham: Springer; 2017. p. 111-9. Xie X, Jiachu D, Liu C, Xie M, Guo J, Cai K, et al. Generating synthesized fluorescein angiography images from color fundus images by generative adversarial networks for macular edema assessment. Transl Vis Sci Technol. 2024;13(9):26. 10.1167/tvst.13.9.26 Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. Adv Neural Inf Process Syst. 2020;33:6840-51. Dhariwal P, Nichol A. Diffusion models beat GANs on image synthesis. Adv Neural Inf Process Syst. 2021;34:8780-94. Song J, Meng C, Ermon S. Denoising diffusion implicit models. arXiv [Preprint]. 2020 Oct 6:arXiv:2010.02502. Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B. High-resolution image synthesis with latent diffusion models. In: Proc IEEE/CVF Conf Comput Vis Pattern Recognit. 2022. p. 10684-95. Nichol AQ, Dhariwal P. Improved denoising diffusion probabilistic models. In: Int Conf Mach Learn. 2021. p. 8162-71. Karras T, Aittala M, Aila T, Laine S. Elucidating the design space of diffusion-based generative models. Adv Neural Inf Process Syst. 2022;35:26565-77. Liu F, Chen D, Guan Z, Zhou X, Zhu J, Ye Q, et al. RemoteCLIP: a vision language foundation model for remote sensing. IEEE Trans Geosci Remote Sens. 2024;62:1-6. 10.1109/TGRS.2024.3387841 Deshpande R, Özbey M, Li H, Anastasio MA, Brooks FJ. Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context. IEEE Trans Med Imaging. 2024;43(10):3608-20. 10.1109/TMI.2024.3412308 Pinaya WHL, Tudosiu PD, Dafflon J, Da Costa PF, Fernandez V, Nachev P, et al. Brain imaging generation with latent diffusion models. In: MICCAI Workshop Deep Generative Models. Cham: Springer Nature Switzerland; 2022. p. 117-26. Khader F, Müller-Franzes G, Tayebi Arasteh S, Han T, Haarburger C, Schulze-Hagen M, et al. Denoising diffusion probabilistic models for 3D medical image generation. Sci Rep. 2023;13(1):7303. 10.1038/s41598-023-34341-2 Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv [Preprint]. 2017 Nov 14:arXiv:1711.05225. Morís DI, Moura JD, Novo J, Ortega M. Adapted generative latent diffusion models for accurate pathological analysis in chest X-ray images. Med Biol Eng Comput. 2024;62(7):2189-212. 10.1007/s11517-024-03064-z Prusty MR, Sudharsan RM, Anand P. Enhancing medical image classification with generative AI using latent denoising diffusion probabilistic model and Wiener filtering approach. Appl Soft Comput. 2024;161:111714. 10.1016/j.asoc.2024.111714 Liu X, Xiang C, Lan L, Li C, Xiao H, Liu Z. Lesion region inpainting: an approach for pseudo-healthy image synthesis in intracranial infection imaging. Front Microbiol. 2024;15:1453870. 10.3389/fmicb.2024.1453870 Chen X, Peng Y. Counterfactual condition diffusion with continuous prior adaptive correction for anomaly detection in multimodal brain MRI. Expert Syst Appl. 2024;254:124295. 10.1016/j.eswa.2024.124295 Dhawan K, Nijhawan SS. Cross-modality synthetic data augmentation using GANs: enhancing brain MRI and chest X-ray classification. medRxiv [Preprint]. 2024:2024.06. Rahman T, Khandakar A, Qiblawey Y, Tahir A, Kiranyaz S, Kashem SB, et al. Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Comput Biol Med. 2021;132:104319. 10.1016/j.compbiomed.2021.104319 Jain S, Agrawal A, Saporta A, Truong SQ, Duong DN, Bui T, et al. RadGraph: extracting clinical entities and relations from radiology reports. arXiv [Preprint]. 2021:arXiv:2106.14463. Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. Adv Neural Inf Process Syst. 2017;30:6626-37. Zhai G, Min X. Perceptual image quality assessment: a survey. Sci China Inf Sci. 2020;63(11):211301. 10.1007/s11432-019-2786-9