{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T23:02:51Z","timestamp":1789686171946,"version":"4.0.1"},"publisher-location":"Cham","reference-count":65,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032370310","type":"print"},{"value":"9783032370327","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\/978-3-032-37032-7_17","type":"book-chapter","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T22:14:23Z","timestamp":1789683263000},"page":"303-321","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TopoAgent: An Agentic Framework for\u00a0Automated Topology Learning in\u00a0Medical Imaging"],"prefix":"10.1007","author":[{"given":"Guangyu","family":"Meng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengfei","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiyu","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erin Wolf","family":"Chambers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danny Z.","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Adame, D., et al.: Topo-VM-UNetV2: encoding topology into vision Mamba UNet for polyp segmentation. In: CBMS, pp. 258\u2013263 (2025)","DOI":"10.1109\/CBMS65348.2025.00059"},{"issue":"8","key":"17_CR2","first-page":"1","volume":"18","author":"H Adams","year":"2017","unstructured":"Adams, H., Emerson, T., Kirby, M., Neville, R., Peterson, C., et al.: Persistence Images: a stable vector representation of persistent homology. J. Mach. Learn. Res. 18(8), 1\u201335 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Ahmed, F.: Topological signatures vs. gradient histograms: a comparative study for medical image classification. arXiv preprint arXiv:2507.03006 (2025)","DOI":"10.1016\/j.bspc.2026.110783"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Ahmed, F., Bhuiyan, M.A.N., Coskunuzer, B.: Topo-CNN: retinal image analysis with topological deep learning. J. Imag. Inform. Med. (2025)","DOI":"10.1101\/2024.02.03.24302291"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Ali, D., Asaad, A., Jimenez, M.J., Nanda, V., Paluzo-Hidalgo, E., Soriano-Trigueros, M.: A survey of vectorization methods in topological data analysis. IEEE Trans. Pattern Anal. Mach. Intell. 45(12) (2023)","DOI":"10.1109\/TPAMI.2023.3308391"},{"key":"17_CR6","unstructured":"Anthropic: The Claude model card (2025). https:\/\/www.anthropic.com\/research\/claude-model-card"},{"key":"17_CR7","unstructured":"Asia Pacific Tele-Ophthalmology Society, APTOS 2019 Blindness Detection (2019). https:\/\/www.kaggle.com\/c\/aptos2019-blindness-detection"},{"issue":"1","key":"17_CR8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"issue":"1","key":"17_CR9","first-page":"77","volume":"16","author":"P Bubenik","year":"2015","unstructured":"Bubenik, P.: Statistical topological data analysis using persistence landscapes. J. Mach. Learn. Res. 16(1), 77\u2013102 (2015)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"17_CR10","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1090\/S0273-0979-09-01249-X","volume":"46","author":"G Carlsson","year":"2009","unstructured":"Carlsson, G.: Topology and data. Bull. Am. Math. Soc. 46(2), 255\u2013308 (2009)","journal-title":"Bull. Am. Math. Soc."},{"key":"17_CR11","unstructured":"Carri\u00e8re, M., Chazal, F., Ike, Y., Lacombe, T., Royer, M., Umeda, Y.: PersLay: a neural network layer for persistence diagrams and new graph topological signatures. In: AISTATS, pp. 2786\u20132796 (2020)"},{"key":"17_CR12","unstructured":"Chambers, E.W., Meng, G.: A stable and theoretically grounded Gromov-Wasserstein distance for Reeb graph comparison using persistence images. arXiv preprint arXiv:2507.01171 (2025)"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: ACM SIGKDD, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Cheng, J., et al.: Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE 10(10), e0140381 (2015)","DOI":"10.1371\/journal.pone.0140381"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Combalia, M., et al.: Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 international skin imaging collaboration grand challenge. Lancet Digit. Health 4(5), e330\u2013e339 (2022)","DOI":"10.1016\/S2589-7500(22)00021-8"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Edelsbrunner, H., Letscher, D., Zomorodian, A.: Topological persistence and simplification. Discrete Comput. Geom. 28(4), 511\u2013533 (2002)","DOI":"10.1007\/s00454-002-2885-2"},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Erickson, N., et al.: TabArena: a living benchmark for machine learning on tabular data. arXiv preprint arXiv:2506.16791 (2025)","DOI":"10.52202\/085713-0519"},{"key":"17_CR18","unstructured":"Fallahpour, A., Ma, J., Munim, A., Lyu, H., Wang, B.: MedRAX: medical reasoning agent for chest X-Ray. arXiv preprint arXiv:2502.02673 (2025)"},{"key":"17_CR19","unstructured":"Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. Adv. Neural. Inf. Process. Syst. 28 (2015)"},{"key":"17_CR20","unstructured":"Google DeepMind: Gemini 2.5: Our most intelligent AI model (2025). https:\/\/deepmind.google\/technologies\/gemini\/"},{"key":"17_CR21","unstructured":"Gorishniy, Y., Kotelnikov, A., Babenko, A.: TabM: advancing tabular deep learning with parameter-efficient ensembling. In: ICLR (2025)"},{"key":"17_CR22","unstructured":"Grattafiori, A., et al.: The Llama 3 herd of models. arXiv preprint arXiv:2407.21783 (2024)"},{"key":"17_CR23","doi-asserted-by":"crossref","unstructured":"Gu, P., et al.: Integrating multi-scale and multi-filtration topological features for medical image classification. In: WACV, pp. 8660\u20138669 (2026)","DOI":"10.1109\/WACV61042.2026.00835"},{"key":"17_CR24","doi-asserted-by":"crossref","unstructured":"Gu, P., Wang, H., Zhang, Y., Li, H., Wang, C., Chen, D.: TopoImages: incorporating local topology encoding into deep learning models for medical image classification. In: ACM MM, pp. 1938\u20131947 (2025)","DOI":"10.1145\/3746027.3755546"},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"17_CR26","doi-asserted-by":"crossref","unstructured":"Hollmann, N., M\u00fcller, S., Purucker, L., Krishnakumar, A., K\u00f6rfer, M., et al.: Accurate predictions on small data with a tabular foundation model. Nature (2025)","DOI":"10.1038\/s41586-024-08328-6"},{"key":"17_CR27","doi-asserted-by":"crossref","unstructured":"Holzm\u00fcller, D., Grinsztajn, L., Obst, N.: Better by default: strong pre-tuned MLPs and boosted trees on tabular data. In: NeurIPS (2024)","DOI":"10.52202\/079017-0837"},{"key":"17_CR28","unstructured":"Hurst, A., Lerer, A., Goucher, A.P., Perelman, A., Ramesh, A., Clark, A., et al.: GPT-4o system card. arXiv preprint arXiv:2410.21276 (2024)"},{"key":"17_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5","volume-title":"Automated Machine Learning: Methods, Systems, Challenges","author":"F Hutter","year":"2019","unstructured":"Hutter, F., Kotthoff, L., Vanschoren, J.: Automated Machine Learning: Methods, Systems, Challenges. Springer, Cham (2019)"},{"key":"17_CR30","doi-asserted-by":"crossref","unstructured":"Khamis, A.: Agentic AI systems: architecture and evaluation using a frictionless parking scenario. IEEE Access (2025)","DOI":"10.1109\/ACCESS.2025.3590264"},{"key":"17_CR31","first-page":"15965","volume":"33","author":"K Kim","year":"2020","unstructured":"Kim, K., Kim, J., Zaheer, M., Kim, J., Chazal, F., Wasserman, L.: PLLay: efficient topological layer based on persistent landscapes. Adv. Neural. Inf. Process. Syst. 33, 15965\u201315977 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"17_CR32","unstructured":"LangChain, Inc.: LangGraph: build resilient language agents as graphs (2024). https:\/\/github.com\/langchain-ai\/langgraph"},{"issue":"1","key":"17_CR33","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1038\/s41598-018-36798-y","volume":"9","author":"P Lawson","year":"2019","unstructured":"Lawson, P., Sholl, A.B., Brown, J.Q., Fasy, B.T., Wenk, C.: Persistent homology for the quantitative evaluation of architectural features in prostate cancer histology. Sci. Rep. 9(1), 1139 (2019)","journal-title":"Sci. Rep."},{"issue":"1","key":"17_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.177","volume":"4","author":"RS Lee","year":"2017","unstructured":"Lee, R.S., Gimenez, F., Hoogi, A., Miyake, K.K., Gorovoy, M., Rubin, D.L.: A curated mammography data set for use in computer-aided detection and diagnosis research. Sci. Data 4(1), 1\u20139 (2017)","journal-title":"Sci. Data"},{"key":"17_CR35","doi-asserted-by":"crossref","unstructured":"Li, B., Yan, T., Pan, Y., Luo, J., Ji, R., et al.: MMedAgent: learning to use medical tools with multi-modal agent. In: Findings of EMNLP, pp. 8745\u20138760 (2024)","DOI":"10.18653\/v1\/2024.findings-emnlp.510"},{"issue":"4","key":"17_CR36","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1109\/TMI.2025.3621093","volume":"45","author":"P Liang","year":"2026","unstructured":"Liang, P., et al.: Cell instance segmentation: the devil is in the boundaries. IEEE Trans. Med. Imaging 45(4), 1311\u20131324 (2026)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"17_CR37","doi-asserted-by":"publisher","first-page":"221","DOI":"10.3934\/fods.2024009","volume":"6","author":"J Liu","year":"2024","unstructured":"Liu, J., Shen, L., Wei, G.W.: ChatGPT for computational topology. Found. Data Sci. 6(2), 221 (2024)","journal-title":"Found. Data Sci."},{"key":"17_CR38","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin Transformer V2: scaling up capacity and resolution. In: CVPR, pp. 12009\u201312019 (2022)","DOI":"10.1109\/CVPR52688.2022.01170"},{"key":"17_CR39","doi-asserted-by":"crossref","unstructured":"Maria, C., Boissonnat, J.D., Glisse, M., Yvinec, M.: GUDHI: simplicial complexes and persistent homology packages. In: ICMS, pp. 167\u2013174 (2014)","DOI":"10.1007\/978-3-662-44199-2_28"},{"key":"17_CR40","doi-asserted-by":"crossref","unstructured":"Mecke, K.R.: Additivity, convexity, and beyond: applications of Minkowski functionals in statistical physics. In: Mecke, K.R., Stoyan, D. (eds.) SP-SS, pp. 111\u2013184, vol. 554. Springer, Heidelberg (2000)","DOI":"10.1007\/3-540-45043-2_6"},{"key":"17_CR41","unstructured":"Meng, G., Gu, P., Liang, P., Lalor, J.P., Chambers, E.W., Chen, D.Z.: TopoCL: topological contrastive learning for medical imaging. In: CVPR, pp. 42681\u201342690 (2026)"},{"key":"17_CR42","doi-asserted-by":"crossref","unstructured":"Meng, G., Zeng, Q., Lalor, J.P., Yu, H.: A psychology-based unified dynamic framework for curriculum learning. Comput. Linguist., 1\u201349 (2025)","DOI":"10.1162\/COLI.a.584"},{"key":"17_CR43","doi-asserted-by":"crossref","unstructured":"Meng, G., et al.: Efficient approximation of Earth Mover\u2019s distance based on nearest neighbor search. IEEE Trans. Multimedia (2025)","DOI":"10.1109\/TMM.2025.3623529"},{"issue":"7","key":"17_CR44","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","volume":"24","author":"T Ojala","year":"2002","unstructured":"Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971\u2013987 (2002)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"17_CR45","doi-asserted-by":"crossref","unstructured":"Peng, Y., Wang, H., Sonka, M., Chen, D.Z.: PHG-Net: persistent homology guided medical image classification. In: WACV, pp. 7583\u20137592 (2024)","DOI":"10.1109\/WACV57701.2024.00741"},{"key":"17_CR46","doi-asserted-by":"crossref","unstructured":"Perea, J.A., Munch, E., Khasawneh, F.A.: Approximating continuous functions on persistence diagrams using template functions. Found. Comput. Math. 23(4), 1215\u20131272 (2023)","DOI":"10.1007\/s10208-022-09567-7"},{"key":"17_CR47","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., Gulin, A.: CatBoost: unbiased boosting with categorical features. Adv. Neural. Inf. Process. Syst. 31 (2018)"},{"key":"17_CR48","unstructured":"Royer, M., Chazal, F., Levrard, C., Umeda, Y., Ike, Y.: ATOL: measure vectorization for automatic topologically-oriented learning. In: AISTATS (2021)"},{"key":"17_CR49","doi-asserted-by":"publisher","first-page":"8634","DOI":"10.52202\/075280-0377","volume":"36","author":"N Shinn","year":"2023","unstructured":"Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K., Yao, S.: Reflexion: language agents with verbal reinforcement learning. Adv. Neural. Inf. Process. Syst. 36, 8634\u20138652 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"17_CR50","doi-asserted-by":"crossref","unstructured":"Singhal, S., et\u00a0al.: Topology-based biomarkers accurately predict breast cancer outcome and survival. Cancer Res. (2026)","DOI":"10.1158\/0008-5472.c.8542716"},{"key":"17_CR51","doi-asserted-by":"crossref","unstructured":"Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1455\u20131462 (2016)","DOI":"10.1109\/TBME.2015.2496264"},{"key":"17_CR52","doi-asserted-by":"crossref","unstructured":"Su, Z., et al.: Topological data analysis and topological deep learning beyond persistent homology: a review. Artif. Intell. Rev. (2025)","DOI":"10.1007\/s10462-025-11462-w"},{"key":"17_CR53","unstructured":"Tang, Y., Wang, K., Chen, Y., Zhou, G.: EndoAgent: a memory-guided reflective agent for intelligent endoscopic vision-to-decision reasoning. arXiv preprint. arXiv:2508.07292 (2025). Withdrawal"},{"issue":"4","key":"17_CR54","first-page":"310","volume":"3","author":"K Turner","year":"2014","unstructured":"Turner, K., Mukherjee, S., Boyer, D.M.: Persistent homology transform for modeling shapes and surfaces. Inf. Inference J. IMA 3(4), 310\u2013344 (2014)","journal-title":"Inf. Inference J. IMA"},{"key":"17_CR55","first-page":"228","volume":"12","author":"Y Umeda","year":"2017","unstructured":"Umeda, Y.: Time series classification via topological data analysis. Inf. Media Technol. 12, 228\u2013239 (2017)","journal-title":"Inf. Media Technol."},{"key":"17_CR56","unstructured":"Wang, G., et al.: Voyager: an open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291 (2023)"},{"key":"17_CR57","doi-asserted-by":"crossref","unstructured":"Wang, H., Huang, G., Zhao, Z., Cheng, L., et al.: CCF-GNN: a unified model aggregating appearance, microenvironment, and topology for pathology image classification. IEEE Trans. Med. Imaging (2023)","DOI":"10.1109\/TMI.2023.3249343"},{"issue":"6","key":"17_CR58","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-024-40231-1","volume":"18","author":"L Wang","year":"2024","unstructured":"Wang, L., et al.: A survey on large language model based autonomous agents. Front. Comp. Sci. 18(6), 186345 (2024)","journal-title":"Front. Comp. Sci."},{"key":"17_CR59","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: MedAgent-Pro: towards evidence-based multi-modal medical diagnosis via reasoning agentic workflow. arXiv preprint arXiv:2503.18968 (2025)","DOI":"10.20944\/preprints202503.1751.v2"},{"issue":"2","key":"17_CR60","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-024-4222-0","volume":"68","author":"Z Xi","year":"2025","unstructured":"Xi, Z., et al.: The rise and potential of large language model based agents: a survey. SCIENCE CHINA Inf. Sci. 68(2), 121101 (2025)","journal-title":"SCIENCE CHINA Inf. Sci."},{"key":"17_CR61","doi-asserted-by":"crossref","unstructured":"Xu, G., et al.: A comprehensive survey of agentic AI in healthcare. Authorea Preprints (2025)","DOI":"10.36227\/techrxiv.176240542.22279040\/v1"},{"key":"17_CR62","doi-asserted-by":"crossref","unstructured":"Yang, J., et al.: MedMNIST v2\u2013a large-scale lightweight benchmark for 2D and 3D biomedical image classification. Sci. Data 10(1), 41 (2023)","DOI":"10.1038\/s41597-022-01721-8"},{"key":"17_CR63","unstructured":"Yao, S., et al.: ReAct: synergizing reasoning and acting in language models. In: ICLR (2022)"},{"key":"17_CR64","unstructured":"Zhuang, J., Li, W., Manivannan, S., Wang, R., et al.: Skin lesion analysis towards melanoma detection using deep neural network ensemble. ISIC Challenge (2018)"},{"issue":"4","key":"17_CR65","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s10462-024-10710-9","volume":"57","author":"A Zia","year":"2024","unstructured":"Zia, A., et al.: Topological deep learning: a review of an emerging paradigm. Artif. Intell. Rev. 57(4), 77 (2024)","journal-title":"Artif. Intell. Rev."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-37032-7_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T22:14:36Z","timestamp":1789683276000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37032-7_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032370310","9783032370327"],"references-count":65,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37032-7_17","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":"18 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malm\u00f6","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}