{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:20:00Z","timestamp":1778080800221,"version":"3.51.4"},"reference-count":77,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Medtech Science and Innovation Centre, Uppsala, Sweden"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1109\/tmi.2024.3368365","type":"journal-article","created":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T18:54:54Z","timestamp":1708541694000},"page":"2599-2609","source":"Crossref","is-referenced-by-count":10,"title":["Multi-Scale Feature Alignment for Continual Learning of Unlabeled Domains"],"prefix":"10.1109","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6527-6823","authenticated-orcid":false,"given":"Kevin","family":"Thandiackal","sequence":"first","affiliation":[{"name":"Computer-Assisted Applications in Medicine Group, ETH Zürich, Zürich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luigi","family":"Piccinelli","sequence":"additional","affiliation":[{"name":"Computer-Assisted Applications in Medicine Group, ETH Zürich, Zürich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajarsi","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pushpak","family":"Pati","sequence":"additional","affiliation":[{"name":"IBM Research Europe, Zürich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8639-7373","authenticated-orcid":false,"given":"Orcun","family":"Goksel","sequence":"additional","affiliation":[{"name":"Computer-Assisted Applications in Medicine Group, ETH Zürich, Zürich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-019-0508-1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-020-00682-w"},{"key":"ref3","first-page":"2136","article-title":"TransMIL: Transformer based correlated multiple instance learning for whole slide image classification","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"34","author":"Shao"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101563"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102264"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19803-8_41"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102915"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01343-4"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2945942"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3400066"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref13","article-title":"Conditional adversarial domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Long"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00151"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58589-1_28"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19830-4_6"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102924"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.01.012"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00568-3"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3057446"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/s0079-7421(08)60536-8"},{"key":"ref22","article-title":"An empirical investigation of catastrophic forgetting in gradient-based neural networks","author":"Goodfellow","year":"2013","journal-title":"arXiv:1312.6211"},{"key":"ref23","article-title":"Continual learning with deep generative replay","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"30","author":"Shin"},{"key":"ref24","article-title":"FearNet: Brain-Inspired Model for Incremental Learning","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Kemker"},{"key":"ref25","article-title":"Memory replay GANs: Learning to generate new categories without forgetting","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"31","author":"Wu"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01158"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00672"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00701"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01623"},{"key":"ref30","article-title":"Generative feature-driven image replay for continual learning","author":"Thandiackal","year":"2021","journal-title":"arXiv:2106.05350"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"ref32","first-page":"3987","article-title":"Continual learning through synaptic intelligence","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zenke"},{"key":"ref33","article-title":"Variational continual learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Nguyen"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"ref35","article-title":"Adapting to continuously shifting domains","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR) Workshop","author":"Bobu"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i3.16370"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00418"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00221"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460982"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00121"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.18"},{"key":"ref43","first-page":"1989","article-title":"CyCADA: Cycle-consistent adversarial domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hoffman"},{"key":"ref44","article-title":"mixup: Beyond empirical risk minimization","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Zhang"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00934-2_23"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3056023"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102473"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref49","first-page":"16481","article-title":"GAN memory with no forgetting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Cong"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-17866-2"},{"key":"ref51","article-title":"CGANs with projection discriminator","author":"Miyato","year":"2018","journal-title":"arXiv:1802.05637"},{"key":"ref52","first-page":"3481","article-title":"Which training methods for GANs do actually converge?","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","volume":"80","author":"Mescheder"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1503.02531"},{"key":"ref57","article-title":"Collection of textures in colorectal cancer histology","author":"Kather","year":"2016","journal-title":"Zenodo"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1038\/srep27988"},{"key":"ref59","article-title":"100,000 histological images of human colorectal cancer and healthy tissue","author":"Kather","year":"2018","journal-title":"Zenodo"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002730"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101696"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref64","first-page":"1079","article-title":"Information-theoretical learning of discriminative clusters for unsupervised domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","volume":"2","author":"Shi"},{"key":"ref65","article-title":"On the variance of the adaptive learning rate and beyond","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Liu"},{"key":"ref66","article-title":"Progressive growing of gans for improved quality, stability, and variation","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Karras"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref68","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Kingma"},{"key":"ref69","article-title":"The unusual effectiveness of averaging in GAN training","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Yazici"},{"key":"ref70","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"32","author":"Paszke"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"ref72","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. NIPS","volume":"33","author":"Ho"},{"key":"ref73","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","volume":"139","author":"Nichol"},{"key":"ref74","first-page":"30105","article-title":"StyleGAN-t: Unlocking the power of GANs for fast large-scale text-to-image synthesis","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","volume":"202","author":"Sauer"},{"key":"ref75","first-page":"5253","article-title":"Extracting training data from diffusion models","volume-title":"Proc. 32nd USENIX Secur. Symp.","author":"Carlini"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref77","article-title":"PATE-GAN: Generating synthetic data with differential privacy guarantees","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Jordon"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/10579886\/10443417.pdf?arnumber=10443417","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T04:36:02Z","timestamp":1719894962000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10443417\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7]]},"references-count":77,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2024.3368365","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7]]}}}