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Unsupervised Super-Resolution: Creating High-Resolution Medical Images from Low-Resolution Anisotropic Examples.

, , and . CoRR, (2020)

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A density-based algorithm for discovering clusters in large spatial databases with noise., , , , and . Kdd, 96, page 226--231. (1996)Biological data annotation via a human-augmenting AI-based labeling system., , , , , , , , , and 4 other author(s). npj Digit. Medicine, (2021)On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study, , , , , , , and . Data Mining and Knowledge Discovery, 30 (4): 891--927 (Jul 1, 2016)Knowledge Discovery in Databases, and . Springer-Verlag, Berlin/Heidelberg, (2000)Density-based clustering., , , and . WIREs Data Mining Knowl. Discov., (2020)Internal Evaluation of Unsupervised Outlier Detection., , , and . ACM Trans. Knowl. Discov. Data, 14 (4): 47:1-47:42 (2020)Unsupervised Super-Resolution: Creating High-Resolution Medical Images from Low-Resolution Anisotropic Examples., , and . CoRR, (2020)Unsupervised super-resolution: creating high-resolution medical images from low-resolution anisotropic examples., , and . Medical Imaging: Image Processing, volume 11596 of SPIE Proceedings, SPIE, (2021)Incremental Clustering for Mining in a Data Warehouse Environment, , , , and . Proc. of 24th VLDB Conference, (1998)Mediengestütztes Bildungsmanagement. Schriften zur EDV-orientierten Betriebswirtschaft Dt. Univ.-Verl. u.a., Wiesbaden, (1999)