{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:26:34Z","timestamp":1771953994494,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032113160","type":"print"},{"value":"9783032113177","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-11317-7_35","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:55:27Z","timestamp":1767322527000},"page":"417-428","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Latent Space Synergy: Text-Guided Data Augmentation for\u00a0Direct Diffusion Biomedical Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5095-605X","authenticated-orcid":false,"given":"Muhammad","family":"Aqeel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1832-297X","authenticated-orcid":false,"given":"Maham","family":"Nazir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7756-8249","authenticated-orcid":false,"given":"Zanxi","family":"Ruan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0015-5534","authenticated-orcid":false,"given":"Francesco","family":"Setti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"35_CR1","doi-asserted-by":"crossref","unstructured":"Aqeel, M., Sharifi, S., Cristani, M., Setti, F.: Meta learning-driven iterative refinement for robust anomaly detection in industrial inspection. In: European Conference on Computer Vision, pp. 445\u2013460. Springer (2024)","DOI":"10.1007\/978-3-031-92805-5_28"},{"key":"35_CR2","doi-asserted-by":"crossref","unstructured":"Aqeel, M., Sharifi, S., Cristani, M., Setti, F.: Self-supervised learning for robust surface defect detection. In: International Conference on Deep Learning Theory and Applications (2024)","DOI":"10.1007\/978-3-031-66705-3_11"},{"key":"35_CR3","doi-asserted-by":"crossref","unstructured":"Aqeel, M., Sharifi, S., Cristani, M., Setti, F.: Self-supervised iterative refinement for anomaly detection in industrial quality control. In: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (2025)","DOI":"10.5220\/0013178100003912"},{"key":"35_CR4","unstructured":"Aqeel, M., Sharifi, S., Cristani, M., Setti, F.: Towards real unsupervised anomaly detection via confident meta-learning. In: Accepted to Proceedings of the IEEE\/CVF International Conference on Computer Vision (2025)"},{"key":"35_CR5","unstructured":"Baranchuk, D., Rubachev, I., Voynov, A., Khrulkov, V., Babenko, A.: Label-efficient semantic segmentation with diffusion models. arXiv preprint arXiv:2112.03126 (2021)"},{"key":"35_CR6","doi-asserted-by":"crossref","unstructured":"Bernal, J., S\u00e1nchez, F.J., Fern\u00e1ndez-Esparrach, G., Gil, D., Rodr\u00edguez, C., Vilari\u00f1o, F.: Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians. Comput. Med. Imaging graph. 43, 99\u2013111 (2015)","DOI":"10.1016\/j.compmedimag.2015.02.007"},{"key":"35_CR7","unstructured":"Chen, J., et al.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"35_CR8","unstructured":"Choi, E., Biswal, S., Malin, B., Duke, J., Stewart, W.F., Sun, J.: Generating multi-label discrete patient records using generative adversarial networks. In: Machine Learning for Healthcare Conference, pp. 286\u2013305. PMLR (2017)"},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Chowdary, G.J., Yin, Z.: Diffusion transformer u-net for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 622\u2013631. Springer (2023)","DOI":"10.1007\/978-3-031-43901-8_59"},{"key":"35_CR10","doi-asserted-by":"crossref","unstructured":"Fan, D.P., et al.: Pranet: Parallel reverse attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-assisted Interventionm, pp. 263\u2013273. Springer (2020)","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"35_CR11","doi-asserted-by":"crossref","unstructured":"Girella, F., Liu, Z., Fummi, F., Setti, F., Cristani, M., Capogrosso, L.: Leveraging latent diffusion models for training-free in-distribution data augmentation for surface defect detection. In: International Conference on Content-Based Multimedia Indexing (CBMI) (2024)","DOI":"10.1109\/CBMI62980.2024.10858875"},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 574\u2013584 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"35_CR13","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"2","key":"35_CR14","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnu-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"Jha, D., et al.: Kvasir-seg: A segmented polyp dataset. In: International conference on multimedia modeling, pp. 451\u2013462. Springer (2019)","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Lin, T., Chen, Z., Yan, Z., Yu, W., Zheng, F.: Stable diffusion segmentation for biomedical images with single-step reverse process. In: Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024, pp. 656\u2013666. Springer Nature Switzerland, Cham (2024)","DOI":"10.1007\/978-3-031-72111-3_62"},{"key":"35_CR17","unstructured":"Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning, pp. 8162\u20138171. PMLR (2021)"},{"key":"35_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101570","volume":"59","author":"JI Orlando","year":"2020","unstructured":"Orlando, J.I., et al.: Refuge challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Med. Image Anal. 59, 101570 (2020)","journal-title":"Med. Image Anal."},{"key":"35_CR19","doi-asserted-by":"crossref","unstructured":"Peebles, W., Xie, S.: Scalable diffusion models with transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4195\u20134205 (2023)","DOI":"10.1109\/ICCV51070.2023.00387"},{"key":"35_CR20","unstructured":"Podell, D., et al.: Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952 (2023)"},{"key":"35_CR21","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"35_CR22","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"4","key":"35_CR23","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1109\/TBME.2022.3216269","volume":"70","author":"P Sharma","year":"2022","unstructured":"Sharma, P., Gautam, A., Maji, P., Pachori, R.B., Balabantaray, B.K.: Li-segpnet: Encoder-decoder mode lightweight segmentation network for colorectal polyps analysis. IEEE Trans. Biomed. Eng. 70(4), 1330\u20131339 (2022)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"35_CR24","doi-asserted-by":"crossref","unstructured":"Shi, W., Xu, J., Gao, P.: Ssformer: A lightweight transformer for semantic segmentation. In: 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP), pp.\u00a01\u20135. IEEE (2022)","DOI":"10.1109\/MMSP55362.2022.9949177"},{"key":"35_CR25","unstructured":"Song, Y., Dhariwal, P., Chen, M., Sutskever, I.: Consistency models (2023)"},{"key":"35_CR26","doi-asserted-by":"crossref","unstructured":"Wei, J., Hu, Y., Zhang, R., Li, Z., Zhou, S.K., Cui, S.: Shallow attention network for polyp segmentation. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24, pp. 699\u2013708. Springer (2021)","DOI":"10.1007\/978-3-030-87193-2_66"},{"key":"35_CR27","unstructured":"Wu, J., et al.: Medsegdiff: Medical image segmentation with diffusion probabilistic model. In: Medical Imaging with Deep Learning (2024)"},{"key":"35_CR28","doi-asserted-by":"crossref","unstructured":"Wu, J., Ji, W., Fu, H., Xu, M., Jin, Y., Xu, Y.: Medsegdiff-v2: Diffusion-based medical image segmentation with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence (2024)","DOI":"10.1609\/aaai.v38i6.28418"},{"key":"35_CR29","unstructured":"Xing, Z., Wan, L., Fu, H., Yang, G., Zhu, L.: Diff-unet: A diffusion embedded network for volumetric segmentation. arXiv preprint arXiv:2303.10326 (2023)"},{"key":"35_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, Z., et al.: Diffboost: Enhancing medical image segmentation via text-guided diffusion model. IEEE Trans. Med. Imaging (2024)","DOI":"10.1109\/TMI.2024.3519307"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing - ICIAP 2025 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-11317-7_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:55:29Z","timestamp":1767322529000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-11317-7_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032113160","9783032113177"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-11317-7_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap.org\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}