{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T10:18:01Z","timestamp":1742984281760,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031458569"},{"type":"electronic","value":"9783031458576"}],"license":[{"start":{"date-parts":[[2023,10,14]],"date-time":"2023-10-14T00:00:00Z","timestamp":1697241600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,14]],"date-time":"2023-10-14T00:00:00Z","timestamp":1697241600000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-45857-6_8","type":"book-chapter","created":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T07:02:34Z","timestamp":1697180554000},"page":"73-83","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Boosting Knowledge Distillation via\u00a0Random Fourier Features for\u00a0Prostate Cancer Grading in\u00a0Histopathology Images"],"prefix":"10.1007","author":[{"given":"Trinh","family":"Thi Le Vuong","sequence":"first","affiliation":[]},{"given":"Jin Tae","family":"Kwak","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,14]]},"reference":[{"issue":"4","key":"8_CR1","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1137\/16M1105396","volume":"38","author":"H Avron","year":"2017","unstructured":"Avron, H., Clarkson, K.L., Woodruff, D.P.: Faster kernel ridge regression using sketching and preconditioning. SIAM J. Matrix Anal. Appl. 38(4), 1116\u20131138 (2017)","journal-title":"SIAM J. Matrix Anal. Appl."},{"issue":"4","key":"8_CR2","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1002\/path.5921","volume":"257","author":"P Bankhead","year":"2022","unstructured":"Bankhead, P.: Developing image analysis methods for digital pathology. J. Pathol. 257(4), 391\u2013402 (2022)","journal-title":"J. Pathol."},{"issue":"1","key":"8_CR3","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1038\/s41591-021-01620-2","volume":"28","author":"W Bulten","year":"2022","unstructured":"Bulten, W., et al.: Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the panda challenge. Nat. Med. 28(1), 154\u2013163 (2022)","journal-title":"Nat. Med."},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Chen, D., Mei, J.P., Zhang, H., Wang, C., Feng, Y., Chen, C.: Knowledge distillation with the reused teacher classifier. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11933\u201311942 (2022)","DOI":"10.1109\/CVPR52688.2022.01163"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Chen, D., et al.: Cross-layer distillation with semantic calibration. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 7028\u20137036 (2021)","DOI":"10.1609\/aaai.v35i8.16865"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Chitta, R., Jin, R., Jain, A.K.: Efficient kernel clustering using random Fourier features. In: 2012 IEEE 12th International Conference on Data Mining, pp. 161\u2013170. IEEE (2012)","DOI":"10.1109\/ICDM.2012.61"},{"key":"8_CR7","unstructured":"Choromanski, K.M., et al.: Rethinking attention with performers. In: International Conference on Learning Representations (2021)"},{"issue":"1","key":"8_CR8","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.eururo.2019.08.005","volume":"77","author":"MB Culp","year":"2020","unstructured":"Culp, M.B., Soerjomataram, I., Efstathiou, J.A., Bray, F., Jemal, A.: Recent global patterns in prostate cancer incidence and mortality rates. Eur. Urol. 77(1), 38\u201352 (2020)","journal-title":"Eur. Urol."},{"key":"8_CR9","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"8_CR10","unstructured":"Huang, Z., Wang, N.: Like what you like: knowledge distill via neuron selectivity transfer. arXiv preprint arXiv:1707.01219 (2017)"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Lee-Thorp, J., Ainslie, J., Eckstein, I., Ontanon, S.: FNet: mixing tokens with Fourier transforms. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4296\u20134313. Association for Computational Linguistics, Seattle (2022)","DOI":"10.18653\/v1\/2022.naacl-main.319"},{"key":"8_CR12","unstructured":"Li, Z., Ton, J.F., Oglic, D., Sejdinovic, D.: Towards a unified analysis of random Fourier features. In: International Conference on Machine Learning, pp. 3905\u20133914. PMLR (2019)"},{"issue":"10","key":"8_CR13","doi-asserted-by":"publisher","first-page":"7128","DOI":"10.1109\/TPAMI.2021.3097011","volume":"44","author":"F Liu","year":"2021","unstructured":"Liu, F., Huang, X., Chen, Y., Suykens, J.A.: Random features for kernel approximation: a survey on algorithms, theory, and beyond. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 7128\u20137148 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"8_CR14","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1145\/3503250","volume":"65","author":"B Mildenhall","year":"2021","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65(1), 99\u2013106 (2021)","journal-title":"Commun. ACM"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Mormont, R., Geurts, P., Mar\u00e9e, R.: Comparison of deep transfer learning strategies for digital pathology. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2262\u20132271 (2018)","DOI":"10.1109\/CVPRW.2018.00303"},{"key":"8_CR16","unstructured":"Munkhoeva, M., Kapushev, Y., Burnaev, E., Oseledets, I.: Quadrature-based features for kernel approximation. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Geiger, A.: GIRAFFE: representing scenes as compositional generative neural feature fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11453\u201311464 (2021)","DOI":"10.1109\/CVPR46437.2021.01129"},{"key":"8_CR18","unstructured":"Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Park, W., Kim, D., Lu, Y., Cho, M.: Relational knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3967\u20133976 (2019)","DOI":"10.1109\/CVPR.2019.00409"},{"issue":"5","key":"8_CR20","doi-asserted-by":"publisher","first-page":"2030","DOI":"10.1109\/TNNLS.2020.2995884","volume":"32","author":"N Passalis","year":"2020","unstructured":"Passalis, N., Tzelepi, M., Tefas, A.: Probabilistic knowledge transfer for lightweight deep representation learning. IEEE Trans. Neural Netw. Learn. Syst. 32(5), 2030\u20132039 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"8_CR21","unstructured":"Peng, H., Pappas, N., Yogatama, D., Schwartz, R., Smith, N., Kong, L.: Random feature attention. In: International Conference on Learning Representations (2021)"},{"key":"8_CR22","unstructured":"Rahimi, A., Recht, B.: Random features for large-scale kernel machines. In: Advances in Neural Information Processing Systems, vol. 20 (2007)"},{"key":"8_CR23","unstructured":"Rawat, A.S., Chen, J., Yu, F.X.X., Suresh, A.T., Kumar, S.: Sampled softmax with random Fourier features. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"8_CR24","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014)"},{"key":"8_CR25","unstructured":"Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th International Conference on Machine Learning, pp. 6105\u20136114 (2019)"},{"key":"8_CR26","unstructured":"Tancik, M., et al.: Fourier features let networks learn high frequency functions in low dimensional domains. In: Advances in Neural Information Processing Systems, vol. 33, pp. 7537\u20137547 (2020)"},{"key":"8_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/978-3-031-25066-8_31","volume-title":"Computer Vision - ECCV 2022 Workshops","author":"TTL Vuong","year":"2023","unstructured":"Vuong, T.T.L., Vu, Q.D., Jahanifar, M., Graham, S., Kwak, J.T., Rajpoot, N.: IMPaSh: a novel domain-shift resistant representation for colorectal cancer tissue classification. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) ECCV 2022, Part III. LNCS, vol. 13803, pp. 543\u2013555. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-25066-8_31"},{"issue":"3","key":"8_CR28","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1109\/JBHI.2021.3099817","volume":"26","author":"TT Vuong","year":"2021","unstructured":"Vuong, T.T., Song, B., Kim, K., Cho, Y.M., Kwak, J.T.: Multi-scale binary pattern encoding network for cancer classification in pathology images. IEEE J. Biomed. Health Inform. 26(3), 1152\u20131163 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"8_CR29","doi-asserted-by":"publisher","first-page":"811044","DOI":"10.3389\/fpubh.2022.811044","volume":"10","author":"L Wang","year":"2022","unstructured":"Wang, L., Lu, B., He, M., Wang, Y., Wang, Z., Du, L.: Prostate cancer incidence and mortality: global status and temporal trends in 89 countries from 2000 to 2019. Front. Public Health 10, 811044 (2022)","journal-title":"Front. Public Health"},{"key":"8_CR30","unstructured":"Yu, F.X.X., Suresh, A.T., Choromanski, K.M., Holtmann-Rice, D.N., Kumar, S.: Orthogonal random features. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"}],"container-title":["Lecture Notes in Computer Science","Domain Adaptation and Representation Transfer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45857-6_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T17:01:48Z","timestamp":1710349308000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45857-6_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,14]]},"ISBN":["9783031458569","9783031458576"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45857-6_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,10,14]]},"assertion":[{"value":"14 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DART","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"MICCAI Workshop on Domain Adaptation and Representation Transfer","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dart2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/dart2023\/home?pli=1","order":11,"name":"conference_url","label":"Conference URL","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","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":"32","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":"16","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":"N\/A% - 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":"2.5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}