{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T04:54:51Z","timestamp":1791435291152,"version":"4.3.3"},"reference-count":42,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007276","name":"ASELSAN Inc.","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007276","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Ulcerative Colitis (UC) lacks longitudinal visual data, which limits both disease progression modeling and the effectiveness of computer-aided diagnosis systems. These systems are further constrained by sparse intermediate disease stages and the discrete nature of the Mayo Endoscopic Score (MES). Meanwhile, synthetic image generation has made significant advances. In this paper, we propose novel ordinal embedding architectures for conditional diffusion models to generate realistic UC progression sequences from cross-sectional endoscopic images. Methods: By adapting Stable Diffusion v1.4 with two specialized ordinal embeddings (Basic Ordinal Embedder using linear interpolation and Additive Ordinal Embedder modeling cumulative pathological features), our framework converts discrete MES categories into continuous progression representations. Results: The Additive Ordinal Embedder outperforms alternatives, achieving superior distributional alignment (CMMD 0.4137, recall 0.6331) and disease consistency comparable to real data (Quadratic Weighted Kappa 0.8425, UMAP Silhouette Score 0.0571). The generated sequences exhibit smooth transitions between severity levels while maintaining anatomical fidelity. Conclusions: This work establishes a foundation for transforming static medical datasets into dynamic progression models and demonstrates that ordinal-aware embeddings can effectively capture disease severity relationships, enabling synthesis of underrepresented intermediate stages. These advances support applications in medical education, diagnosis, and synthetic data generation.<\/jats:p>","DOI":"10.3390\/diagnostics15202558","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:34:08Z","timestamp":1760114048000},"page":"2558","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Progressive Disease Image Generation with Ordinal-Aware Diffusion Models"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5411-2249","authenticated-orcid":false,"given":"Meryem Mine","family":"Kurt","sequence":"first","affiliation":[{"name":"ASELSAN Inc., Ankara 06200, Turkey"},{"name":"Graduate School of Informatics, Middle East Technical University, Ankara 06800, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0391-3847","authenticated-orcid":false,"given":"\u00dcmit Mert","family":"\u00c7a\u011flar","sequence":"additional","affiliation":[{"name":"Graduate School of Informatics, Middle East Technical University, Ankara 06800, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6082-2573","authenticated-orcid":false,"given":"Alptekin","family":"Temizel","sequence":"additional","affiliation":[{"name":"Graduate School of Informatics, Middle East Technical University, Ankara 06800, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ochiai, K., Ozawa, T., Shibata, J., Ishihara, S., and Tada, T. (2022). Current status of artificial intelligence-based computer-assisted diagnosis systems for gastric cancer in endoscopy. Diagnostics, 12.","DOI":"10.3390\/diagnostics12123153"},{"key":"ref_2","unstructured":"Li, L., Qiu, J., Saha, A., Li, L., Li, P., He, M., Guo, Z., and Yuan, W. (2024). Artificial intelligence for biomedical video generation. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"20552076221149651","DOI":"10.1177\/20552076221149651","article-title":"Digital twin in healthcare: Recent updates and challenges","volume":"9","author":"Sun","year":"2023","journal-title":"Digit. Health"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"34749","DOI":"10.1109\/JIOT.2024.3421918","article-title":"Generative AI-driven human digital twin in IoT-healthcare: A comprehensive survey","volume":"11","author":"Chen","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_5","unstructured":"Schulam, P., and Arora, R. (2016). Disease trajectory maps. Advances in Neural Information Processing Systems 29, 30th Annual Conference on Neural Information Processing Systems 2016, Barcelona, Spain, 5\u201310 December 2016, NeurIPS."},{"key":"ref_6","unstructured":"Lim, B., and van der Schaar, M. (2018, January 17\u201318). Disease-atlas: Navigating disease trajectories using deep learning. Proceedings of the Machine Learning for Healthcare Conference, PMLR, Palo Alto, CA, USA."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bhagwat, N., Viviano, J.D., Voineskos, A.N., Chakravarty, M.M., and Alzheimer\u2019s Disease Neuroimaging Initiative (2018). Modeling and prediction of clinical symptom trajectories in Alzheimer\u2019s disease using longitudinal data. PLoS Comput. Biol., 14.","DOI":"10.1371\/journal.pcbi.1006376"},{"key":"ref_8","unstructured":"Cao, X., Liang, K., Liao, K.D., Gao, T., Ye, W., Chen, J., Ding, Z., Cao, J., Rehg, J.M., and Sun, J. (2024). Medical video generation for disease progression simulation. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1136\/gut.2005.082909","article-title":"The Montreal classification of inflammatory bowel disease: Controversies, consensus, and implications","volume":"55","author":"Satsangi","year":"2006","journal-title":"Gut"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1016\/j.mayocp.2019.01.018","article-title":"Ulcerative Colitis","volume":"94","author":"Fuerstein","year":"2019","journal-title":"Mayo Clin. Proc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1093\/gastro\/gox016","article-title":"Ulcerative Colitis Endoscopic Index of Severity (UCEIS) versus Mayo Endoscopic Score (MES) in guiding the need for colectomy in patients with acute severe colitis","volume":"6","author":"Xie","year":"2018","journal-title":"Gastroenterol. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.21037\/dmr-21-1","article-title":"A close view on histopathological changes in inflammatory bowel disease, a narrative review","volume":"4","author":"Kellermann","year":"2021","journal-title":"Dig. Med. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"109061","DOI":"10.1016\/j.patcog.2022.109061","article-title":"Conditional GAN with 3D discriminator for MRI generation of Alzheimer\u2019s disease progression","volume":"133","author":"Jung","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"102257","DOI":"10.1016\/j.media.2021.102257","article-title":"Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia","volume":"75","author":"Ravi","year":"2022","journal-title":"Med Image Anal."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1038\/s41591-024-02838-6","article-title":"Generative models improve fairness of medical classifiers under distribution shifts","volume":"30","author":"Ktena","year":"2024","journal-title":"Nat. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102846","DOI":"10.1016\/j.media.2023.102846","article-title":"Diffusion models in medical imaging: A comprehensive survey","volume":"88","author":"Kazerouni","year":"2023","journal-title":"Med Image Anal."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"M\u00fcller-Franzes, G., Niehues, J.M., Khader, F., Arasteh, S.T., Haarburger, C., Kuhl, C., Wang, T., Han, T., Nolte, T., and Nebelung, S. (2023). A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-39278-0"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Nie, B., and Zhang, G. (2025). Ulcerative Severity Estimation Based on Advanced CNN\u2013Transformer Hybrid Models. Appl. Sci., 15.","DOI":"10.3390\/app15137484"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/s00535-018-1439-1","article-title":"Evidence-based clinical practice guidelines for inflammatory bowel disease","volume":"53","author":"Matsuoka","year":"2018","journal-title":"J. Gastroenterol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1111\/den.14334","article-title":"Systematic review of artificial intelligence-based image diagnosis for inflammatory bowel disease","volume":"34","author":"Kawamoto","year":"2022","journal-title":"Dig. Endosc."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gong, E.J., and Bang, C.S. (2025). Edge Artificial Intelligence Device in Real-Time Endoscopy for the Classification of Colonic Neoplasms. Diagnostics, 15.","DOI":"10.3390\/diagnostics15121478"},{"key":"ref_22","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-Encoding Variational Bayes. arXiv."},{"key":"ref_23","unstructured":"van den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K. (2016). Pixel Recurrent Neural Networks. arXiv."},{"key":"ref_24","first-page":"73","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019, January 15\u201320). A style-based generator architecture for generative adversarial networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T. (2020, January 13\u201319). Analyzing and improving the image quality of stylegan. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref_27","first-page":"8780","article-title":"Diffusion models beat gans on image synthesis","volume":"34","author":"Dhariwal","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"\u00c7a\u011flar, \u00dc.M., \u0130nci, A., Hano\u011flu, O., Polat, G., and Temizel, A. (2023, January 5\u20138). Ulcerative Colitis Mayo Endoscopic Scoring Classification with Active Learning and Generative Data Augmentation. Proceedings of the 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye.","DOI":"10.1109\/BIBM58861.2023.10385621"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103362","DOI":"10.1016\/j.media.2024.103362","article-title":"Texture-preserving diffusion model for CBCT-to-CT synthesis","volume":"99","author":"Zhang","year":"2025","journal-title":"Med. Image Anal."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Takezaki, S., and Uchida, S. (2024, January 27\u201330). An Ordinal Diffusion Model for Generating Medical Images with Different Severity Levels. Proceedings of the 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece.","DOI":"10.1109\/ISBI56570.2024.10635504"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, C., Liu, H., Liu, Y., Feng, B.Y., Li, W., Liu, X., Chen, Z., Shao, J., and Yuan, Y. (2024, January 6\u201310). Endora: Video generation models as endoscopy simulators. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Marrakesh, Morocco.","DOI":"10.1007\/978-3-031-72089-5_22"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100851","DOI":"10.1016\/j.disamonth.2019.02.004","article-title":"A comprehensive review and update on Ulcerative Colitis","volume":"65","author":"Gajendran","year":"2019","journal-title":"Dis. Mon."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022, January 18\u201324). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref_34","unstructured":"Polat, G., Kani, H., Ergenc, I., Alahdab, Y., Temizel, A., and Atug, O. (2022). Labeled Images for Ulcerative Colitis (Limuc) Dataset, Zenodo."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Karras, T., Aittala, M., Lehtinen, J., Hellsten, J., Aila, T., and Laine, S. (2024, January 16\u201322). Analyzing and improving the training dynamics of diffusion models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.02282"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hang, T., Gu, S., Li, C., Bao, J., Chen, D., Hu, H., Geng, X., and Guo, B. (2023, January 2\u20136). Efficient diffusion training via min-snr weighting strategy. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00684"},{"key":"ref_37","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jayasumana, S., Ramalingam, S., Veit, A., Glasner, D., Chakrabarti, A., and Kumar, S. (2024, January 16\u201322). Rethinking fid: Towards a better evaluation metric for image generation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00889"},{"key":"ref_39","unstructured":"Kynk\u00e4\u00e4nniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T. (2019). Improved precision and recall metric for assessing generative models. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., and Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. arXiv.","DOI":"10.21105\/joss.00861"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yuan, X., and Zhou, Y. (2024, January 3\u20136). EIVS: Unpaired Endoscopy Image Virtual Staining via State Space Generative Model. Proceedings of the 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Lisbon, Portugal.","DOI":"10.1109\/BIBM62325.2024.10822811"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Martyniak, S., Kaleta, J., Dall\u2019Alba, D., Naskr\u0119t, M., P\u0142otka, S., and Korzeniowski, P. (March, January 26). Simuscope: Realistic endoscopic synthetic dataset generation through surgical simulation and diffusion models. Proceedings of the 2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Tucson, AZ, USA.","DOI":"10.1109\/WACV61041.2025.00419"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/20\/2558\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:16:04Z","timestamp":1760242564000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/20\/2558"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,10]]},"references-count":42,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["diagnostics15202558"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15202558","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,10]]}}}