{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:37:56Z","timestamp":1762648676044,"version":"build-2065373602"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:00:00Z","timestamp":1762646400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:00:00Z","timestamp":1762646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004829","name":"Sichuan Provincial Department of Science and Technology","doi-asserted-by":"crossref","award":["Nos.2024ZYD0089, 2023YFQ0011","Nos.2024ZYD0089, 2023YFQ0011","Nos.2024ZYD0089, 2023YFQ0011","Nos.2024ZYD0089, 2023YFQ0011","Nos.2024ZYD0089, 2023YFQ0011","Nos.2024ZYD0089, 2023YFQ0011","Nos.2024ZYD0089, 2023YFQ0011"],"award-info":[{"award-number":["Nos.2024ZYD0089, 2023YFQ0011","Nos.2024ZYD0089, 2023YFQ0011","Nos.2024ZYD0089, 2023YFQ0011","Nos.2024ZYD0089, 2023YFQ0011","Nos.2024ZYD0089, 2023YFQ0011","Nos.2024ZYD0089, 2023YFQ0011","Nos.2024ZYD0089, 2023YFQ0011"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Yibin University Science and Technology Project","award":["Nos.2022YY01","Nos.2022YY01","Nos.2022YY01","Nos.2022YY01","Nos.2022YY01","Nos.2022YY01","Nos.2022YY01"],"award-info":[{"award-number":["Nos.2022YY01","Nos.2022YY01","Nos.2022YY01","Nos.2022YY01","Nos.2022YY01","Nos.2022YY01","Nos.2022YY01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"DOI":"10.1007\/s11227-025-08027-7","type":"journal-article","created":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:33:24Z","timestamp":1762648404000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A diffusion probabilistic model with multi-scale conditional fusion for enhanced medical image segmentation"],"prefix":"10.1007","volume":"81","author":[{"given":"Peng","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaorong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xudong","family":"Ling","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengqing","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihua","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Libin","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,9]]},"reference":[{"key":"8027_CR1","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) 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, Springer, pp. 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"8027_CR2","doi-asserted-by":"crossref","unstructured":"Alam M.\u00a0S, Wang D, Liao Q, Sowmya A (2022) A multi-scale context aware attention model for medical image segmentation, IEEE J Biomed Health Inf","DOI":"10.1109\/JBHI.2022.3227540"},{"key":"8027_CR3","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et\u00a0al (2020) An image is worth 16x16 words: Transformers for image recognition at scale, arXiv preprint arXiv:2010.11929"},{"key":"8027_CR4","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows, In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"8027_CR5","doi-asserted-by":"crossref","unstructured":"Yan H, Zhang E, Wang J, Leng C, Basu A, Peng J (2023) Hybrid conv-vit network for hyperspectral image classification, IEEE Geosci Remote Sens Lett","DOI":"10.1109\/LGRS.2023.3287277"},{"key":"8027_CR6","doi-asserted-by":"crossref","unstructured":"Huo Y, Jin K, Cai J, Xiong H, Pang J (2023) Vision transformer (vit)-based applications in image classification, In: 2023 IEEE 9th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing,(HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), IEEE, pp. 135\u2013140","DOI":"10.1109\/BigDataSecurity-HPSC-IDS58521.2023.00033"},{"key":"8027_CR7","doi-asserted-by":"crossref","unstructured":"Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M (2022) Swin-unet: Unet-like pure transformer for medical image segmentation, In: European Conference on Computer Vision, Springer, pp. 205\u2013218","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"8027_CR8","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu C.-Y, Feichtenhofer C, Darrell T, Xie S (2022) A convnet for the 2020s, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"8027_CR9","first-page":"15","volume":"15","author":"S Saha","year":"2018","unstructured":"Saha S (2018) A comprehensive guide to convolutional neural networks-the eli5 way. Towards Data Sci 15:15","journal-title":"Towards Data Sci"},{"key":"8027_CR10","doi-asserted-by":"publisher","first-page":"109512","DOI":"10.1016\/j.knosys.2022.109512","volume":"253","author":"Z Han","year":"2022","unstructured":"Han Z, Jian M, Wang G-G (2022) Convunext: An efficient convolution neural network for medical image segmentation. Knowl-Based Syst 253:109512","journal-title":"Knowl-Based Syst"},{"key":"8027_CR11","doi-asserted-by":"crossref","unstructured":"Volokitin A, Erdil E, Karani N, Tezcan K.C, Chen X, Van\u00a0Gool L, Konukoglu E (2020) Modelling the distribution of 3d brain mri using a 2d slice vae, In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part VII 23, Springer, pp. 657\u2013666","DOI":"10.1007\/978-3-030-59728-3_64"},{"key":"8027_CR12","doi-asserted-by":"crossref","unstructured":"Yan W, Wang Y, Gu S, Huang L, Yan F, Xia L, Tao Q (2019) The domain shift problem of medical image segmentation and vendor-adaptation by unet-gan, In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, Proceedings, Part II 22, Springer, 2019, pp. 623\u2013631","DOI":"10.1007\/978-3-030-32245-8_69"},{"issue":"11","key":"8027_CR13","doi-asserted-by":"publisher","first-page":"104005","DOI":"10.1016\/j.imavis.2020.104005","volume":"104","author":"K Liu","year":"2020","unstructured":"Liu K, Qiu G, Tang W, Zhou F (2020) Spectral regularization for combating mode collapse in gans. Image Vis Comput 104(11):104005","journal-title":"Image Vis Comput"},{"key":"8027_CR14","doi-asserted-by":"crossref","unstructured":"Luo Z, Chen D, Zhang Y, Huang Y, Wang L, Shen Y, Zhao D, Zhou J, Tan T (2023) Videofusion: Decomposed diffusion models for high-quality video generation, In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10209\u201310218","DOI":"10.1109\/CVPR52729.2023.00984"},{"key":"8027_CR15","doi-asserted-by":"crossref","unstructured":"Shang S, Shan Z, Liu G, Zhang J (2023) Resdiff: Combining cnn and diffusion model for image super-resolution, arXiv preprint arXiv:2303.08714","DOI":"10.1609\/aaai.v38i8.28746"},{"key":"8027_CR16","doi-asserted-by":"crossref","unstructured":"Lugmayr A, Danelljan M, Romero A, Yu F, Timofte R, Van\u00a0Gool L (2022) Repaint: Inpainting using denoising diffusion probabilistic models, In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11461\u201311471","DOI":"10.1109\/CVPR52688.2022.01117"},{"key":"8027_CR17","unstructured":"Amit T, Shaharbany T, Nachmani E, Wolf L (2021) Segdiff: Image segmentation with diffusion probabilistic models, arXiv preprint arXiv:2112.00390"},{"key":"8027_CR18","doi-asserted-by":"crossref","unstructured":"Kawar B, Zada S, Lang O, Tov O, Chang H, Dekel T, Mosseri I, Irani M (2023) Imagic: Text-based real image editing with diffusion models, In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6007\u20136017","DOI":"10.1109\/CVPR52729.2023.00582"},{"key":"8027_CR19","unstructured":"Wolleb J, Sandk\u00fchler R, Bieder F, Valmaggia P, Cattin P.C (2022) Diffusion models for implicit image segmentation ensembles, In: International Conference on Medical Imaging with Deep Learning, PMLR, pp. 1336\u20131348"},{"key":"8027_CR20","doi-asserted-by":"crossref","unstructured":"Chung H, Ryu D, McCann M.T, Klasky M.L, Ye J.C (2023) Solving 3d inverse problems using pre-trained 2d diffusion models, In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 22542\u201322551","DOI":"10.1109\/CVPR52729.2023.02159"},{"key":"8027_CR21","unstructured":"Sasaki H, Willcocks C.G, Breckon T.P (2021) Unit-ddpm: Unpaired image translation with denoising diffusion probabilistic models, arXiv preprint arXiv:2104.05358"},{"key":"8027_CR22","doi-asserted-by":"crossref","unstructured":"Choi J, Kim S, Jeong Y, Gwon Y, Yoon S (2021) Ilvr: Conditioning method for denoising diffusion probabilistic models, arXiv preprint arXiv:2108.02938","DOI":"10.1109\/ICCV48922.2021.01410"},{"key":"8027_CR23","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho J, Jain A, Abbeel P (2020) Denoising diffusion probabilistic models. Adv Neural Inf Process Syst 33:6840\u20136851","journal-title":"Adv Neural Inf Process Syst"},{"key":"8027_CR24","unstructured":"Wu J, Fang H, Zhang Y, Yang Y, Xu Y (2022) Medsegdiff: Medical image segmentation with diffusion probabilistic model, arXiv preprint arXiv:2211.00611"},{"key":"8027_CR25","first-page":"36479","volume":"35","author":"C Saharia","year":"2022","unstructured":"Saharia C, Chan W, Saxena S, Li L, Whang J, Denton EL, Ghasemipour K, Gontijo Lopes R, Karagol Ayan B, Salimans T et al (2022) Photorealistic text-to-image diffusion models with deep language understanding. Adv Neural Inf Process Syst 35:36479\u201336494","journal-title":"Adv Neural Inf Process Syst"},{"key":"8027_CR26","doi-asserted-by":"crossref","unstructured":"Avrahami O, Lischinski D, Fried O (2022) Blended diffusion for text-driven editing of natural images, In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18208\u201318218","DOI":"10.1109\/CVPR52688.2022.01767"},{"key":"8027_CR27","unstructured":"Radford A, Kim J.W, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, et\u00a0al (2021) Learning transferable visual models from natural language supervision, In: International Conference on Machine Learning, PMLR, pp. 8748\u20138763"},{"key":"8027_CR28","doi-asserted-by":"crossref","unstructured":"Li B, Xue K, Liu B, Lai Y.K (2022) Vqbb: Image-to-image translation with vector quantized brownian bridge, arXiv e-prints","DOI":"10.1109\/CVPR52729.2023.00194"},{"key":"8027_CR29","doi-asserted-by":"crossref","unstructured":"Esser P, Rombach R, Ommer B (2021) Taming transformers for high-resolution image synthesis, In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12873\u201312883","DOI":"10.1109\/CVPR46437.2021.01268"},{"issue":"11","key":"8027_CR30","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144","journal-title":"Commun ACM"},{"key":"8027_CR31","unstructured":"Nichol A.\u00a0Q, Dhariwal P (2021) Improved denoising diffusion probabilistic models, In: International Conference on Machine Learning, PMLR, pp. 8162\u20138171"},{"key":"8027_CR32","doi-asserted-by":"crossref","unstructured":"Wang X, Yu K, Wu S, Gu J, Liu Y, Dong C, Qiao Y, Change\u00a0Loy C (2018) Esrgan: Enhanced super-resolution generative adversarial networks, In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops, pp. 0\u20130","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"8027_CR33","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.compmedimag.2019.02.005","volume":"74","author":"S Yu","year":"2019","unstructured":"Yu S, Xiao D, Frost S, Kanagasingam Y (2019) Robust optic disc and cup segmentation with deep learning for glaucoma detection. Comput Med Imaging Graph 74:61\u201371","journal-title":"Comput Med Imaging Graph"},{"key":"8027_CR34","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"8027_CR35","doi-asserted-by":"publisher","first-page":"107404","DOI":"10.1016\/j.patcog.2020.107404","volume":"106","author":"X Qin","year":"2020","unstructured":"Qin X, Zhang Z, Huang C, Dehghan M, Zaiane OR, Jagersand M (2020) U2-net: Going deeper with nested u-structure for salient object detection. Pattern Recogn 106:107404","journal-title":"Pattern Recogn"},{"key":"8027_CR36","unstructured":"Kenton J.\u00a0D. M.-W.\u00a0C, Toutanova L.\u00a0K (2019) Bert: Pre-training of deep bidirectional transformers for language understanding, In: Proceedings of NAACL-HLT, Vol.\u00a01, p.\u00a02"},{"key":"8027_CR37","doi-asserted-by":"crossref","unstructured":"Ning M, Bian C, Yuan C, Ma K, Zheng Y (2021) Ensembled resunet for anatomical brain barriers segmentation, In: Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data: MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4\u20138, 2020, Proceedings 23, Springer, pp. 27\u201333","DOI":"10.1007\/978-3-030-71827-5_3"},{"issue":"12","key":"8027_CR38","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8027_CR39","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille A.L, Zhou Y (2021) Transunet: Transformers make strong encoders for medical image segmentation, arXiv preprint arXiv:2102.04306"},{"key":"8027_CR40","doi-asserted-by":"crossref","unstructured":"Pedraza L, Vargas C, Narv\u00e1ez F, Dur\u00e1n O, Mu\u00f1oz E, Romero E (2015) An open access thyroid ultrasound image database, In: 10th International Symposium on Medical Information Processing and Analysis, Vol. 9287, SPIE, pp. 188\u2013193","DOI":"10.1117\/12.2073532"},{"key":"8027_CR41","unstructured":"Baid U, Ghodasara S, Mohan S, Bilello M, Calabrese E, Colak E, Farahani K, Kalpathy-Cramer J, Kitamura F.C, Pati S, et\u00a0al (2021) The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification, arXiv preprint arXiv:2107.02314"},{"key":"8027_CR42","doi-asserted-by":"crossref","unstructured":"Zhou H.-Y, Yu Y, Wang C, Zhang S, Gao Y, Pan J, Shao J, Lu G, Zhang K, Li W (2023) A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics, Nat Biomed Eng, 1\u201313","DOI":"10.1038\/s41551-023-01045-x"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08027-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-025-08027-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-025-08027-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:33:30Z","timestamp":1762648410000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-025-08027-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,9]]},"references-count":42,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["8027"],"URL":"https:\/\/doi.org\/10.1007\/s11227-025-08027-7","relation":{},"ISSN":["1573-0484"],"issn-type":[{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,9]]},"assertion":[{"value":"18 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"1544"}}