{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T07:00:43Z","timestamp":1771743643543,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049704","type":"print"},{"value":"9783032049711","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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\/978-3-032-04971-1_15","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T17:09:51Z","timestamp":1758301791000},"page":"154-164","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DetectDiffuse: Aggregation- and Attention-Driven Universal Lesion Detection with Multi-scale Diffusion Model"],"prefix":"10.1007","author":[{"given":"Xinyu","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danni","family":"Ai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingfan","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyu","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deqiang","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"15_CR1","unstructured":"Baid, U., et al.: The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)"},{"key":"15_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102680","volume":"84","author":"P Bilic","year":"2023","unstructured":"Bilic, P., et al.: The liver tumor segmentation benchmark (LiTS). Med. Image Anal. 84, 102680 (2023)","journal-title":"Med. Image Anal."},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European Conference on Computer Vision, pp. 213\u2013229. Springer (2020)","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Chen, S., Sun, P., Song, Y., Luo, P.: Diffusiondet: diffusion model for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 19830\u201319843 (2023)","DOI":"10.1109\/ICCV51070.2023.01816"},{"issue":"7","key":"15_CR5","doi-asserted-by":"publisher","first-page":"1558","DOI":"10.1109\/TBME.2016.2613502","volume":"64","author":"Q Dou","year":"2016","unstructured":"Dou, Q., Chen, H., Yu, L., Qin, J., Heng, P.A.: Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans. Biomed. Eng. 64(7), 1558\u20131567 (2016)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"15_CR6","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256. JMLR Workshop and Conference Proceedings (2010)"},{"key":"15_CR7","doi-asserted-by":"publisher","first-page":"7389","DOI":"10.1109\/TIP.2020.3002345","volume":"29","author":"T Kong","year":"2020","unstructured":"Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J.: Foveabox: beyound anchor-based object detection. IEEE Trans. Image Process. 29, 7389\u20137398 (2020)","journal-title":"IEEE Trans. Image Process."},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Li, H., Chen, L., Han, H., Kevin\u00a0Zhou, S.: SATr: slice attention with transformer for universal lesion detection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 163\u2013174. Springer (2022)","DOI":"10.1007\/978-3-031-16437-8_16"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Li, Z., Zhang, S., Zhang, J., Huang, K., Wang, Y., Yu, Y.: MVP-Net: multi-view FPN with position-aware attention for deep universal lesion detection. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI: 22nd International Conference, Shenzhen, China, 13\u201317 October 2019, Proceedings, Part VI 22, pp. 13\u201321. Springer (2019)","DOI":"10.1007\/978-3-030-32226-7_2"},{"key":"15_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2021.104255","volume":"102","author":"Z Liu","year":"2021","unstructured":"Liu, Z., et al.: MLANet: multi-layer anchor-free network for generic lesion detection. Eng. Appl. Artif. Intell. 102, 104255 (2021)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"6","key":"15_CR11","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"15_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102605","volume":"82","author":"HR Roth","year":"2022","unstructured":"Roth, H.R., et al.: Rapid artificial intelligence solutions in a pandemic\u2013the COVID-19-20 lung CT lesion segmentation challenge. Med. Image Anal. 82, 102605 (2022)","journal-title":"Med. Image Anal."},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Sheoran, M., Dani, M., Sharma, M., Vig, L.: DKMA-ULD: domain knowledge augmented multi-head attention based robust universal lesion detection. arXiv preprint arXiv:2203.06886 (2022)","DOI":"10.5244\/C.35.344"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Sheoran, M., Dani, M., Sharma, M., Vig, L.: An efficient anchor-free universal lesion detection in CT-scans. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp.\u00a01\u20134. IEEE (2022)","DOI":"10.1109\/ISBI52829.2022.9761698"},{"key":"15_CR15","unstructured":"Simpson, A.L., et al.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019)"},{"issue":"8","key":"15_CR16","doi-asserted-by":"publisher","first-page":"2303","DOI":"10.1109\/JBHI.2020.2964016","volume":"24","author":"L Sun","year":"2020","unstructured":"Sun, L., Wang, J., Huang, Y., Ding, X., Greenspan, H., Paisley, J.: An adversarial learning approach to medical image synthesis for lesion detection. IEEE J. Biomed. Health Inform. 24(8), 2303\u20132314 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Tang, Y.B., Yan, K., Tang, Y.X., Liu, J., Xiao, J., Summers, R.M.: Uldor: a universal lesion detector for CT scans with pseudo masks and hard negative example mining. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 833\u2013836. IEEE (2019)","DOI":"10.1109\/ISBI.2019.8759478"},{"issue":"1 suppl","key":"15_CR18","first-page":"4S","volume":"45","author":"DW Townsend","year":"2004","unstructured":"Townsend, D.W., Carney, J.P., Yap, J.T., Hall, N.C.: PET\/CT today and tomorrow. J. Nucl. Med. 45(1 suppl), 4S-14S (2004)","journal-title":"J. Nucl. Med."},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Wang, B., Qi, G., Tang, S., Zhang, L., Deng, L., Zhang, Y.: Automated pulmonary nodule detection: high sensitivity with few candidates. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 759\u2013767. Springer (2018)","DOI":"10.1007\/978-3-030-00934-2_84"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Wang, X., Han, S., Chen, Y., Gao, D., Vasconcelos, N.: Volumetric attention for 3D medical image segmentation and detection. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI: 22nd International Conference, Shenzhen, China, 13\u201317 October 2019, Proceedings, Part VI 22, pp. 175\u2013184. Springer (2019)","DOI":"10.1007\/978-3-030-32226-7_20"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Yan, K., Bagheri, M., Summers, R.M.: 3D context enhanced region-based convolutional neural network for end-to-end lesion detection. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI: 21st International Conference, Granada, Spain, 16\u201320 September 2018, Proceedings, Part I, pp. 511\u2013519. Springer (2018)","DOI":"10.1007\/978-3-030-00928-1_58"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Yan, K., et al.: Mulan: multitask universal lesion analysis network for joint lesion detection, tagging, and segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 194\u2013202. Springer (2019)","DOI":"10.1007\/978-3-030-32226-7_22"},{"issue":"3","key":"15_CR23","doi-asserted-by":"publisher","first-page":"036501","DOI":"10.1117\/1.JMI.5.3.036501","volume":"5","author":"K Yan","year":"2018","unstructured":"Yan, K., Wang, X., Lu, L., Summers, R.M.: Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J. Med. Imaging 5(3), 036501 (2018)","journal-title":"J. Med. Imaging"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Yang, J., He, Y., Kuang, K., Lin, Z., Pfister, H., Ni, B.: Asymmetric 3D context fusion for universal lesion detection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 571\u2013580. Springer (2021)","DOI":"10.1007\/978-3-030-87240-3_55"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Zhao, P., Li, H., Jin, R., Zhou, S.K.: Diffuld: diffusive universal lesion detection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 94\u2013105. Springer (2023)","DOI":"10.1007\/978-3-031-43904-9_10"},{"key":"15_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100357","volume":"19","author":"W Zhao","year":"2020","unstructured":"Zhao, W., Jiang, D., Queralta, J.P., Westerlund, T.: MSS U-Net: 3D segmentation of kidneys and tumors from CT images with a multi-scale supervised u-net. Inform. Med. Unlocked 19, 100357 (2020)","journal-title":"Inform. Med. Unlocked"},{"key":"15_CR27","unstructured":"Zhou, X., Wang, D., Kr\u00e4henb\u00fchl, P.: Objects as points. arXiv preprint arXiv:1904.07850 (2019)"},{"key":"15_CR28","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04971-1_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T06:44:55Z","timestamp":1771742695000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04971-1_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049704","9783032049711"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04971-1_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}