{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:04:32Z","timestamp":1772910272890,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":40,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819608393","type":"print"},{"value":"9789819608409","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T00:00:00Z","timestamp":1734048000000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-0840-9_11","type":"book-chapter","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T17:29:13Z","timestamp":1734024553000},"page":"155-170","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["RPE-Diff: A Relative Position Encoding Diffusion Model for\u00a0Perirenal Fat Segmentation in\u00a0Metabolic Syndrome"],"prefix":"10.1007","author":[{"given":"Shuai","family":"Ye","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianming","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frank","family":"Kulwa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyu","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md Mamunur","family":"Rahaman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcin","family":"Grzegorzek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongzan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European conference on computer vision. pp. 205\u2013218. Springer (2022)","DOI":"10.1007\/978-3-031-25066-8_9"},{"issue":"10","key":"11_CR2","doi-asserted-by":"publisher","first-page":"2983","DOI":"10.3390\/nu12102983","volume":"12","author":"S Castro-Barquero","year":"2020","unstructured":"Castro-Barquero, S., Ruiz-Le\u00f3n, A.M., Sierra-P\u00e9rez, M., Estruch, R., Casas, R.: Dietary strategies for metabolic syndrome: a comprehensive review. Nutrients 12(10), 2983 (2020)","journal-title":"Nutrients"},{"key":"11_CR3","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"issue":"2","key":"11_CR4","doi-asserted-by":"publisher","first-page":"786","DOI":"10.3390\/ijms23020786","volume":"23","author":"G Fahed","year":"2022","unstructured":"Fahed, G., Aoun, L., Bou Zerdan, M., Allam, S., Bou Zerdan, M., Bouferraa, Y., Assi, H.I.: Metabolic syndrome: updates on pathophysiology and management in 2021. Int. J. Mol. Sci. 23(2), 786 (2022)","journal-title":"Int. J. Mol. Sci."},{"issue":"7","key":"11_CR5","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1507\/endocrj.EJ23-0160","volume":"70","author":"Y Fujishima","year":"2023","unstructured":"Fujishima, Y., Kita, S., Nishizawa, H., Maeda, N., Shimomura, I.: Cardiovascular significance of adipose-derived adiponectin and liver-derived xanthine oxidoreductase in metabolic syndrome. Endocr. J. 70(7), 663\u2013675 (2023)","journal-title":"Endocr. J."},{"issue":"11","key":"11_CR6","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.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"11_CR7","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":"11_CR8","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":"11_CR9","doi-asserted-by":"crossref","unstructured":"Jha, D., Smedsrud, P.H., Riegler, M.A., Johansen, D., De\u00a0Lange, T., Halvorsen, P., Johansen, H.D.: Resunet++: An advanced architecture for medical image segmentation. In: 2019 IEEE international symposium on multimedia (ISM). pp. 225\u20132255. IEEE (2019)","DOI":"10.1109\/ISM46123.2019.00049"},{"key":"11_CR10","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"issue":"2","key":"11_CR11","volume":"79","author":"F Kulwa","year":"2022","unstructured":"Kulwa, F., Li, C., Grzegorzek, M., Rahaman, M., Shirahama, K., Kosov, S.: Segmentation of Weakly Visible Environmental Microorganism Images Using Pair-wise Deep Learning Features. Biomed. Signal Process. Control 79(2), 104168 (2022)","journal-title":"Biomed. Signal Process. Control"},{"issue":"1","key":"11_CR12","doi-asserted-by":"publisher","first-page":"100243","DOI":"10.1109\/ACCESS.2019.2930111","volume":"7","author":"F Kulwa","year":"2019","unstructured":"Kulwa, F., Li, C., Zhao, X., Cai, B., Xu, N., Qi, S., Chen, S., Teng, Y.: A State-of-the-art Survey for Microorganism Image Segmentation Methods and Future Potential. IEEE Access 7(1), 100243\u2013100269 (2019)","journal-title":"IEEE Access"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Lemieux, I., Despr\u00e9s, J.P.: Metabolic syndrome: past, present and future (2020)","DOI":"10.3390\/nu12113501"},{"key":"11_CR14","doi-asserted-by":"publisher","first-page":"4809","DOI":"10.1007\/s10462-021-10121-0","volume":"55","author":"X Li","year":"2022","unstructured":"Li, X., Li, C., Rahaman, M.M., Sun, H., Li, X., Wu, J., Yao, Y., Grzegorzek, M.: A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches. Artif. Intell. Rev. 55, 4809\u20134878 (2022)","journal-title":"Artif. Intell. Rev."},{"issue":"7","key":"11_CR15","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1177\/0003319718799967","volume":"70","author":"BX Liu","year":"2019","unstructured":"Liu, B.X., Sun, W., Kong, X.Q.: Perirenal fat: a unique fat pad and potential target for cardiovascular disease. Angiology 70(7), 584\u2013593 (2019)","journal-title":"Angiology"},{"issue":"3","key":"11_CR16","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.3390\/su13031224","volume":"13","author":"X Liu","year":"2021","unstructured":"Liu, X., Song, L., Liu, S., Zhang, Y.: A review of deep-learning-based medical image segmentation methods. Sustainability 13(3), 1224 (2021)","journal-title":"Sustainability"},{"issue":"3","key":"11_CR17","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.3390\/su13031224","volume":"13","author":"X Liu","year":"2021","unstructured":"Liu, X., Song, L., Liu, S., Zhang, Y.: A review of deep-learning-based medical image segmentation methods. Sustainability 13(3), 1224 (2021)","journal-title":"Sustainability"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Ma, D., Li, C., Du, T., Qiao, L., Tang, D., Ma, Z., Shi, L., Lu, G., Meng, Q., Chen, Z., Grzegorzek, M., Sun, H.: PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhage. Computers in Biology and Medicine p. Online first (2024)","DOI":"10.1016\/j.compbiomed.2024.108342"},{"key":"11_CR19","unstructured":"Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.Y., Ermon, S.: Sdedit: Guided image synthesis and editing with stochastic differential equations. In: International Conference on Learning Representations (2021)"},{"issue":"10","key":"11_CR20","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"11_CR21","unstructured":"Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International conference on machine learning. pp. 8162\u20138171. PMLR (2021)"},{"key":"11_CR22","unstructured":"Nichol, A.Q., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., Mcgrew, B., Sutskever, I., Chen, M.: Glide: Towards photorealistic image generation and editing with text-guided diffusion models. In: International Conference on Machine Learning. pp. 16784\u201316804. PMLR (2022)"},{"key":"11_CR23","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et\u00a0al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"11_CR24","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":"11_CR25","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":"3","key":"11_CR26","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1038\/s41574-019-0310-7","volume":"16","author":"R Ross","year":"2020","unstructured":"Ross, R., Neeland, I.J., Yamashita, S., Shai, I., Seidell, J., Magni, P., Santos, R.D., Arsenault, B., Cuevas, A., Hu, F.B., et al.: Waist circumference as a vital sign in clinical practice: a consensus statement from the ias and iccr working group on visceral obesity. Nat. Rev. Endocrinol. 16(3), 177\u2013189 (2020)","journal-title":"Nat. Rev. Endocrinol."},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Saharia, C., Chan, W., Chang, H., Lee, C., Ho, J., Salimans, T., Fleet, D., Norouzi, M.: Palette: Image-to-image diffusion models. In: ACM SIGGRAPH 2022 conference proceedings. pp. 1\u201310 (2022)","DOI":"10.1145\/3528233.3530757"},{"issue":"4","key":"11_CR28","first-page":"4713","volume":"45","author":"C Saharia","year":"2022","unstructured":"Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, M.: Image super-resolution via iterative refinement. IEEE Trans. Pattern Anal. Mach. Intell. 45(4), 4713\u20134726 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Shi, L., Li, X., Hu, W., Chen, H., Chen, J., Fan, Z., Gao, M., Jing, Y., Lu, G., Ma, D., Ma, Z., Meng, Q., Tang, D., Sun, H., Grzegorzek, M., Qi, S., Teng, Y., author), C.L.: EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks . Frontiers in Medicine 10, 3389 (2023)","DOI":"10.3389\/fmed.2023.1114673"},{"key":"11_CR30","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: International Conference on Learning Representations (2020)"},{"issue":"4","key":"11_CR31","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1016\/j.bbe.2020.09.008","volume":"40","author":"C Sun","year":"2020","unstructured":"Sun, C., Li, C., Zhang, J., Rahaman, M.M., Ai, S., Chen, H., Kulwa, F., Li, Y., Li, X., Jiang, T.: Gastric Histopathology Image Segmentation Using a Hierarchical Conditional Random Field. Biocybernetics and Biomedical Engineering 40(4), 1535\u20131555 (2020)","journal-title":"Biocybernetics and Biomedical Engineering"},{"key":"11_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108217","volume":"171","author":"D Tang","year":"2024","unstructured":"Tang, D., Li, C., Du, T., Jiang, H., Ma, D., Ma, Z., Grzegorzek, M., Jiang, T., Sun, H.: ECPC-IDS: A Benchmark Endometrail Cancer PET\/CT Image Dataset for Evaluation of Semantic Segmentation and Detection of Hypermetabolic Regions. Comput. Biol. Med. 171, 108217 (2024)","journal-title":"Comput. Biol. Med."},{"key":"11_CR33","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017)"},{"issue":"7","key":"11_CR34","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1109\/TMI.2004.828354","volume":"23","author":"SK Warfield","year":"2004","unstructured":"Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation. IEEE Trans. Med. Imaging 23(7), 903\u2013921 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"11_CR35","unstructured":"Wolleb, J., Sandk\u00fchler, R., Bieder, F., Valmaggia, P., Cattin, P.C.: Diffusion models for implicit image segmentation ensembles. In: International Conference on Medical Imaging with Deep Learning. pp. 1336\u20131348. PMLR (2022)"},{"key":"11_CR36","doi-asserted-by":"crossref","unstructured":"Wu, K., Peng, H., Chen, M., Fu, J., Chao, H.: Rethinking and improving relative position encoding for vision transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 10033\u201310041 (2021)","DOI":"10.1109\/ICCV48922.2021.00988"},{"issue":"18","key":"11_CR37","doi-asserted-by":"publisher","first-page":"9321","DOI":"10.3390\/app12189321","volume":"12","author":"H Yang","year":"2022","unstructured":"Yang, H., Zhao, X., Jiang, T., Zhang, J., Zhao, P., Chen, A., Grzegorzek, M., Li, C.: Comparative Study for Patch-level and Pixel-level Segmentation of Deep Learning Methods on Transparent Images of Environmental Microorganisms: from Convolutional Neural Networks to Visual Transformers. Appl. Sci. 12(18), 9321 (2022)","journal-title":"Appl. Sci."},{"key":"11_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patcog.2021.107885","volume":"115","author":"J Zhang","year":"2021","unstructured":"Zhang, J., Li, C., Kosov, S., Grzegorzek, M., Shirahama, K., Jiang, T., Sun, C., Li, Z., Li, H.: LCU-Net: A Novel Low-cost U-Net for Environmental Microorganism Image Segmentation. Pattern Recogn. 115, 1\u201317 (2021)","journal-title":"Pattern Recogn."},{"issue":"5","key":"11_CR39","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual u-net. IEEE Geosci. Remote Sens. Lett. 15(5), 749\u2013753 (2018)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"issue":"6","key":"11_CR40","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2019","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856\u20131867 (2019)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0840-9_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T18:08:30Z","timestamp":1734026910000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0840-9_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,13]]},"ISBN":["9789819608393","9789819608409"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0840-9_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,13]]},"assertion":[{"value":"13 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2024.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}