{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T12:29:02Z","timestamp":1742992142405,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164514"},{"type":"electronic","value":"9783031164521"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-16452-1_8","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T21:25:46Z","timestamp":1663277146000},"page":"77-87","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Adversarially Robust Prototypical Few-Shot Segmentation with\u00a0Neural-ODEs"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6594-9685","authenticated-orcid":false,"given":"Prashant","family":"Pandey","sequence":"first","affiliation":[]},{"given":"Aleti","family":"Vardhan","sequence":"additional","affiliation":[]},{"given":"Mustafa","family":"Chasmai","sequence":"additional","affiliation":[]},{"given":"Tanuj","family":"Sur","sequence":"additional","affiliation":[]},{"given":"Brejesh","family":"Lall","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Li, F.-F., Rob, F., Pietro, P.: One-shot learning of object categories. IEEE TPAMI, vol. 28 (2006)","DOI":"10.1109\/TPAMI.2006.79"},{"key":"8_CR2","unstructured":"Ian, J., Goodfellow, J.S., Szegedy, C.: Explaining and harnessing adversarial examples. In: ICLR (2015)"},{"key":"8_CR3","unstructured":"Landman, B., Xu, Z., Igelsias, J.E., Styner, M., Robin, Thomas, Langerak, A.K.: Miccai multi-atlas labeling beyond the cranial vault-workshop and challenge. In: MICCAI Multi-Atlas Labeling Beyond Cranial Vault-Workshop Challenge (2015)"},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Kurakin, A., Goodfellow, I.J., Bengio, S.: Adversarial examples in the physical world. In: ICLR (Workshop) (2017)","DOI":"10.1201\/9781351251389-8"},{"key":"8_CR6","unstructured":"Snell, J., Swersky, K., Zemel, R.S.: Prototypical networks for few-shot learning. In: NeurIPS (2017)"},{"key":"8_CR7","unstructured":"Ravi, S.: Hugo Larochelle. Optimization as a model for few-shot learning. In: ICLR (2017)"},{"key":"8_CR8","unstructured":"Kurakin, A., Goodfellow, I.J., Bengio, S.: Adversarial machine learning at scale. In: ICLR (2017)"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O.: Pascal Frossard. Universal adversarial perturbations. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.17"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., Yuille, A.: Adversarial examples for semantic segmentation and object detection. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.153"},{"key":"8_CR11","unstructured":"Dong, N., Xing, E.P.: Few-shot semantic segmentation with prototype learning. In: BMVC (2018)"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H.S., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"8_CR13","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: ICLR (2018)"},{"key":"8_CR14","unstructured":"Ricky, T.Q., Chen, Y.R., Bettencourt, J., Duvenaud, D.: Neural ordinary differential equations. In: NeurIPS (2018)"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Paschali, M., Conjeti, S., Navarro, F., Navab, N.: Generalizability vs. Investigating medical imaging networks using adversarial examples. In: MICCAI, Robustness (2018)","DOI":"10.1007\/978-3-030-00928-1_56"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Arnab, A., Miksik, O., Torr, P.H.S.: On the robustness of semantic segmentation models to adversarial attacks. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00099"},{"key":"8_CR17","unstructured":"Zhang, H., Yu, Y., Jiao, J., Xing, E., El Ghaoui, L., Jordan, M.: Theoretically principled trade-off between robustness and accuracy. In: ICML (2019)"},{"key":"8_CR18","unstructured":"Zhang, H., Chen, H., Song, Z., Boning, D., Dhillon, I., Hsieh, C.-J.: The limitations of adversarial training and the blind-spot attack. In: ICLR (2019)"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Wang, K., Liew, J.H., Zou, Y., Zhou, D., Feng, J.: Panet: few-shot image semantic segmentation with prototype alignment. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00929"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Zhao, A., Balakrishnan, G., Durand, F., Guttag, J.V., Dalca, A.V.: Data augmentation using learned transformations for one-shot medical image segmentation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00874"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Ouyang, C., Kamnitsas, K., Biffi, C., Duan, J., Rueckert, D.: Data efficient unsupervised domain adaptation for cross-modality image segmentation. In: MICCAI (2019)","DOI":"10.1007\/978-3-030-32245-8_74"},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Ozbulak, U., Van Messem, A., De Neve, W.: Impact of adversarial examples on deep learning models for biomedical image segmentation. In: MICCAI (2019)","DOI":"10.1007\/978-3-030-32245-8_34"},{"key":"8_CR23","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)"},{"key":"8_CR24","doi-asserted-by":"crossref","unstructured":"Roy, A.G., Siddiqui, S., P\u00f6lsterl, S., Navab, N., Wachinger, C.: \u2018Squeeze & Excite\u2019 Guided few-shot segmentation of volumetric images. In: MedIA, vol. 59 (2020)","DOI":"10.1016\/j.media.2019.101587"},{"key":"8_CR25","doi-asserted-by":"publisher","unstructured":"Rister, B., Yi, D., Shivakumar, K., Nobashi, T., Rubin, D.L.: CT-ORG, a new dataset for multiple organ segmentation in computed tomography. Sci. Data (2020). https:\/\/doi.org\/10.1038\/s41597-020-00715-8","DOI":"10.1038\/s41597-020-00715-8"},{"key":"8_CR26","doi-asserted-by":"crossref","unstructured":"Li, X., Wei, T., Chen, Y.P., Tai, Y.-W., Tang, C.-K.: FSS-1000: a 1000-class dataset for few-shot segmentation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00294"},{"key":"8_CR27","unstructured":"Yan, H., Du, J., Vincent, Y.F.T., Feng, J.: On robustness of neural ordinary differential equations. In: ICLR (2020)"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Liu, X., Xiao, T., Si, S., Cao, Q., Kumar, S., Hsieh, C.-J.: How does noise help robustness? Explanation and exploration under the neural SDE framework. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00036"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Goldblum, M., Fowl, L., Goldstein, T.: A meta-learning approach. In: NeurIPS, Adversarially Robust Few-Shot Learning (2020)","DOI":"10.1609\/aaai.v34i04.5816"},{"issue":"22","key":"8_CR30","doi-asserted-by":"publisher","first-page":"8079","DOI":"10.3390\/app10228079","volume":"10","author":"S Park","year":"2020","unstructured":"Park, S., So, J.: On the effectiveness of adversarial training in defending against adversarial example attacks for image classification. Appl. Sci. 10(22), 8079 (2020). https:\/\/doi.org\/10.3390\/app10228079","journal-title":"Appl. Sci."},{"key":"8_CR31","unstructured":"Kang, Q., Song, Y., Ding, Q., Tay, W.P.: Stable neural ode with Lyapunov-stable equilibrium points for defending against adversarial attacks. In: NeurIPS (2021)"},{"key":"8_CR32","doi-asserted-by":"crossref","unstructured":"Tang, H., Liu, X., Sun, S., Yan, X., Xie, X.: Recurrent mask refinement for few-shot medical image segmentation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00389"},{"key":"8_CR33","unstructured":"Qi, G., Gong, L., Song, Y., Ma, K., Zheng, Y.: Stabilized medical image attacks. In: ICLR (2021)"},{"key":"8_CR34","unstructured":"Xiaogang, X., Zhao, H., Jia, J.: Dynamic divide-and-conquer adversarial training for robust semantic segmentation. In: ICCV (2021)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16452-1_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T11:42:38Z","timestamp":1710243758000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16452-1_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164514","9783031164521"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16452-1_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"574","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}