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J. Neur. Syst."],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:p> Typically, deep learning models for image segmentation tasks are trained using large datasets of images annotated at the pixel level, which can be expensive and highly time-consuming. A way to reduce the amount of annotated images required for training is to adopt a semi-supervised approach. In this regard, generative deep learning models, concretely Generative Adversarial Networks (GANs), have been adapted to semi-supervised training of segmentation tasks. This work proposes MaskGDM, a deep learning architecture combining some ideas from EditGAN, a GAN that jointly models images and their segmentations, together with a generative diffusion model. With careful integration, we find that using a generative diffusion model can improve EditGAN performance results in multiple segmentation datasets, both multi-class and with binary labels. According to the quantitative results obtained, the proposed model improves multi-class image segmentation when compared to the EditGAN and DatasetGAN models, respectively, by [Formula: see text] and [Formula: see text]. Moreover, using the ISIC dataset, our proposal improves the results from other models by up to [Formula: see text] for the binary image segmentation approach. <\/jats:p>","DOI":"10.1142\/s0129065724500576","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T05:54:20Z","timestamp":1720158860000},"source":"Crossref","is-referenced-by-count":29,"title":["Semi-Supervised Semantic Image Segmentation by Deep Diffusion Models and Generative Adversarial Networks"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4412-9434","authenticated-orcid":false,"given":"Jos\u00e9 \u00c1ngel","family":"D\u00edaz-Franc\u00e9s","sequence":"first","affiliation":[{"name":"ITIS Software, University of M\u00e1laga, Calle Arquitecto Francisco Pe\u00f1alosa 18, M\u00e1laga 29010, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3702-2230","authenticated-orcid":false,"given":"Jos\u00e9 David","family":"Fern\u00e1ndez-Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"ITIS Software, University of M\u00e1laga, Calle Arquitecto Francisco Pe\u00f1alosa 18, M\u00e1laga 29010, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6519-1213","authenticated-orcid":false,"given":"Karl","family":"Thurnhofer-Hemsi","sequence":"additional","affiliation":[{"name":"ITIS Software, University of M\u00e1laga, Calle Arquitecto Francisco Pe\u00f1alosa 18, M\u00e1laga 29010, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8231-5687","authenticated-orcid":false,"given":"Ezequiel","family":"L\u00f3pez-Rubio","sequence":"additional","affiliation":[{"name":"ITIS Software, University of M\u00e1laga, Calle Arquitecto Francisco Pe\u00f1alosa 18, M\u00e1laga 29010, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2024,8,15]]},"reference":[{"key":"S0129065724500576BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3190448"},{"key":"S0129065724500576BIB002","doi-asserted-by":"publisher","DOI":"10.1159\/000512985"},{"key":"S0129065724500576BIB003","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12494"},{"key":"S0129065724500576BIB004","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065723500673"},{"key":"S0129065724500576BIB005","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12647"},{"key":"S0129065724500576BIB006","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065723500168"},{"key":"S0129065724500576BIB007","doi-asserted-by":"publisher","DOI":"10.1142\/S012906572450014X"},{"key":"S0129065724500576BIB008","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065723500132"},{"key":"S0129065724500576BIB009","doi-asserted-by":"publisher","DOI":"10.1515\/revneuro-2020-0043"},{"key":"S0129065724500576BIB010","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-023-02032-0"},{"key":"S0129065724500576BIB011","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104234"},{"key":"S0129065724500576BIB012","first-page":"8162","volume-title":"Int. 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