{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T21:05:50Z","timestamp":1765487150447},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685434","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,25]]},"abstract":"<jats:p>This study delves into the characterization of synthetic lung nodules using latent diffusion models applied to chest CT scans. Our experiments involve guiding the diffusion process by means of a binary mask for localization and various nodule attributes. In particular, the mask indicates the approximate position of the nodule in the shape of a bounding box, while the other scalar attributes are encoded in an embedding vector. The diffusion model operates in 2D, producing a single synthetic CT slice during inference. The architecture comprises a VQ-VAE encoder to convert between the image and latent spaces, and a U-Net responsible for the denoising process. Our primary objective is to assess the quality of synthesized images as a function of the conditional attributes. We discuss possible biases and whether the model adequately positions and characterizes synthetic nodules. Our findings on the capabilities and limitations of the proposed approach may be of interest for downstream tasks involving limited datasets with non-uniform observations, as it is often the case for medical imaging.<\/jats:p>","DOI":"10.3233\/faia240408","type":"book-chapter","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T09:47:53Z","timestamp":1727689673000},"source":"Crossref","is-referenced-by-count":1,"title":["Characterization of Synthetic Lung Nodules in Conditional Latent Diffusion of Chest CT Scans"],"prefix":"10.3233","author":[{"given":"Roger","family":"Mar\u00ed Molas","sequence":"first","affiliation":[{"name":"Eurecat, Centre Tecnol\u00f2gic de Catalunya, Multimedia Technologies, Barcelona, Spain"}]},{"given":"Paula","family":"Sub\u00edas-Beltr\u00e1n","sequence":"additional","affiliation":[{"name":"Eurecat, Centre Tecnol\u00f2gic de Catalunya, Digital Health, Barcelona, Spain"}]},{"given":"Carla","family":"Pitarch Abaigar","sequence":"additional","affiliation":[{"name":"Eurecat, Centre Tecnol\u00f2gic de Catalunya, Digital Health, Barcelona, Spain"}]},{"given":"Mar","family":"Galofr\u00e9 Cardo","sequence":"additional","affiliation":[{"name":"Eurecat, Centre Tecnol\u00f2gic de Catalunya, Digital Health, Barcelona, Spain"}]},{"given":"Rafael","family":"Redondo Tejedor","sequence":"additional","affiliation":[{"name":"Eurecat, Centre Tecnol\u00f2gic de Catalunya, Multimedia Technologies, Barcelona, Spain"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Artificial Intelligence Research and Development"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240408","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T09:47:53Z","timestamp":1727689673000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240408"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,25]]},"ISBN":["9781643685434"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240408","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,25]]}}}