{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T14:58:06Z","timestamp":1776956286558,"version":"3.51.4"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031264375","type":"print"},{"value":"9783031264382","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"vor","delay-in-days":53,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Generative models are becoming popular for the synthesis of medical images. Recently, neural diffusion models have demonstrated the potential to generate photo-realistic images of objects. However, their potential to generate medical images is not explored yet. We explore the possibilities of synthesizing medical images using neural diffusion models. First, we use a pre-trained DALLE2 model to generate lungs X-Ray and CT images from an input text prompt. Second, we train a stable diffusion model with 3165 X-Ray images and generate synthetic images. We evaluate the synthetic image data through a qualitative analysis where two independent radiologists label randomly chosen samples from the generated data as real, fake, or unsure. Results demonstrate that images generated with the diffusion model can translate characteristics that are otherwise very specific to certain medical conditions in chest X-Ray or CT images. Careful tuning of the model can be very promising. To the best of our knowledge, this is the first attempt to generate lungs X-Ray and CT images using neural diffusion models. This work aims to introduce a new dimension in artificial intelligence for medical imaging. Given that this is a new topic, the paper will serve as an introduction and motivation for the research community to explore the potential of diffusion models for medical image synthesis. We have released the synthetic images on <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/www.kaggle.com\/datasets\/hazrat\/awesomelungs\">https:\/\/www.kaggle.com\/datasets\/hazrat\/awesomelungs<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/978-3-031-26438-2_3","type":"book-chapter","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T06:32:56Z","timestamp":1677047576000},"page":"32-39","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Spot the\u00a0Fake Lungs: Generating Synthetic Medical Images Using Neural Diffusion Models"],"prefix":"10.1007","author":[{"given":"Hazrat","family":"Ali","sequence":"first","affiliation":[]},{"given":"Shafaq","family":"Murad","sequence":"additional","affiliation":[]},{"given":"Zubair","family":"Shah","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101552","volume":"58","author":"X Yi","year":"2019","unstructured":"Yi, X., Walia, E., Babyn, P.: Generative adversarial network in medical imaging: a review. 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Big Data"},{"issue":"1","key":"3_CR4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13244-021-01133-z","volume":"13","author":"H Ali","year":"2022","unstructured":"Ali, H., et al.: The role of generative adversarial networks in brain MRI: a scoping review. Insights Imaging 13(1), 1\u201315 (2022)","journal-title":"Insights Imaging"},{"issue":"1","key":"3_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-13658-4","volume":"12","author":"W Ahmad","year":"2022","unstructured":"Ahmad, W., Ali, H., Shah, Z., Azmat, S.: A new generative adversarial network for medical images super resolution. Sci. Rep. 12(1), 1\u201320 (2022)","journal-title":"Sci. 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In: Advances in Neural Information Processing Systems, vol. 34, pp. 8780\u20138794 (2021)"},{"key":"3_CR9","unstructured":"Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 (2022)"},{"key":"3_CR10","unstructured":"Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)"},{"key":"3_CR11","unstructured":"Han, X., Zheng, H., Zhou, M.: CARD: classification and regression diffusion models. arXiv preprint arXiv:2206.07275 (2022)"},{"key":"3_CR12","unstructured":"Pinaya, W.H., et al.: Brain imaging generation with latent diffusion models. arXiv preprint arXiv:2209.07162 (2022)"},{"key":"3_CR13","unstructured":"O\u2019Connor, R.: How to run stable diffusion locally to generate images. https:\/\/www.assemblyai.com\/. 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