{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T06:23:16Z","timestamp":1758349396926,"version":"3.44.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049360"},{"type":"electronic","value":"9783032049377"}],"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\/978-3-032-04937-7_43","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:39:51Z","timestamp":1758260391000},"page":"453-462","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pathology-Informed Latent Diffusion Model for\u00a0Anomaly Detection in\u00a0Lymph Node Metastasis"],"prefix":"10.1007","author":[{"given":"Jiamu","family":"Wang","sequence":"first","affiliation":[]},{"given":"Keunho","family":"Byeon","sequence":"additional","affiliation":[]},{"given":"Jinsol","family":"Song","sequence":"additional","affiliation":[]},{"given":"Anh","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Sangjeong","family":"Ahn","sequence":"additional","affiliation":[]},{"given":"Sung Hak","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Jin Tae","family":"Kwak","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Nathanson, S.D.: Insights into the mechanisms of lymph node metastasis. Cancer 98(2), 413\u2013423 (2003)","key":"43_CR1","DOI":"10.1002\/cncr.11464"},{"doi-asserted-by":"crossref","unstructured":"Budginaite, E., et al.: Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: a systematic-narrative hybrid review. J. Pathol. Inform. 15, 100367 (2024)","key":"43_CR2","DOI":"10.1016\/j.jpi.2024.100367"},{"unstructured":"Salehi, M., et al.: A unified survey on anomaly, novelty, open-set, and out-of-distribution detection: solutions and future challenges. arXiv preprint arXiv:2110.14051 (2021)","key":"43_CR3"},{"unstructured":"Kingma, D.P.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)","key":"43_CR4"},{"key":"43_CR5","first-page":"20685","volume":"33","author":"Z Xiao","year":"2020","unstructured":"Xiao, Z., Yan, Q., Amit, Y.: Likelihood regret: an out-of-distribution detection score for variational auto-encoder. Adv. Neural. Inf. Process. Syst. 33, 20685\u201320696 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"unstructured":"Ren, J., et al.: Likelihood ratios for out-of-distribution detection. In: Advances in Neural Information Processing Systems, vol. 32 (2019)","key":"43_CR6"},{"unstructured":"Serr\u00e0, J., et al.: Input complexity and out-of-distribution detection with likelihood-based generative models. arXiv preprint arXiv:1909.11480 (2019)","key":"43_CR7"},{"unstructured":"Hinton, G.E., Zemel, R.: Autoencoders, minimum description length and Helmholtz free energy. In: Advances in Neural Information Processing Systems, vol. 6 (1993)","key":"43_CR8"},{"key":"43_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/978-3-319-59081-3_23","volume-title":"Advances in Neural Networks \u2013 ISNN 2017","author":"YS Chong","year":"2017","unstructured":"Chong, Y.S., Tay, Y.H.: Abnormal event detection in videos using spatiotemporal autoencoder. In: Cong, F., Leung, A., Wei, Q. (eds.) ISNN 2017. LNCS, vol. 10262, pp. 189\u2013196. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59081-3_23"},{"doi-asserted-by":"crossref","unstructured":"Gong, D., et al.: Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2019)","key":"43_CR10","DOI":"10.1109\/ICCV.2019.00179"},{"doi-asserted-by":"crossref","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","key":"43_CR11","DOI":"10.1145\/3422622"},{"doi-asserted-by":"crossref","unstructured":"Schlegl, T., et al.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: International Conference on Information Processing in Medical Imaging. Springer International Publishing, Cham (2017)","key":"43_CR12","DOI":"10.1007\/978-3-319-59050-9_12"},{"doi-asserted-by":"crossref","unstructured":"Schlegl, T., et al.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30\u201344 (2019)","key":"43_CR13","DOI":"10.1016\/j.media.2019.01.010"},{"key":"43_CR14","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."},{"doi-asserted-by":"crossref","unstructured":"Wyatt, J., et al.: AnoDDPM: anomaly detection with denoising diffusion probabilistic models using simplex noise. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2022)","key":"43_CR15","DOI":"10.1109\/CVPRW56347.2022.00080"},{"doi-asserted-by":"crossref","unstructured":"Graham, M.S., et al.: Denoising diffusion models for out-of-distribution detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2023)","key":"43_CR16","DOI":"10.1109\/CVPRW59228.2023.00296"},{"doi-asserted-by":"crossref","unstructured":"Linmans, J., et al.: Diffusion models for out-of-distribution detection in digital pathology. Med. Image Anal. 93, 103088 (2024)","key":"43_CR17","DOI":"10.1016\/j.media.2024.103088"},{"doi-asserted-by":"crossref","unstructured":"Rombach, R., et al.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2022)","key":"43_CR18","DOI":"10.1109\/CVPR52688.2022.01042"},{"doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Vision-language models for vision tasks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","key":"43_CR19","DOI":"10.1109\/TPAMI.2024.3369699"},{"unstructured":"Lu, M.Y., et al.: Towards a visual-language foundation model for computational pathology. arXiv preprint arXiv:2307.12914 (2023)","key":"43_CR20"},{"doi-asserted-by":"crossref","unstructured":"Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. Jama 318(22), 2199\u20132210 (2017)","key":"43_CR21","DOI":"10.1001\/jama.2017.14580"},{"unstructured":"Heusel, M., et al.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017)","key":"43_CR22"},{"unstructured":"Platen, P., Patil, S., Lozhkov, A., et al.: Diffusers: state-of-the-art diffusion models by (2022), available on GitHub as a repository named huggingface\/diffusers","key":"43_CR23"},{"unstructured":"Liu, L., et al.: Pseudo numerical methods for diffusion models on manifolds. arXiv preprint arXiv:2202.09778 (2022)","key":"43_CR24"},{"doi-asserted-by":"crossref","unstructured":"Virtanen, P., et al.: SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods 17(3), 261\u2013272 (2020)","key":"43_CR25","DOI":"10.1038\/s41592-019-0686-2"}],"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\/978-3-032-04937-7_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:39:58Z","timestamp":1758260398000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04937-7_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049360","9783032049377"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04937-7_43","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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"}}]}}