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Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data.

, , , , , , , , и . IEEE Trans. Med. Imaging, 38 (4): 1037-1047 (2019)

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Katharina Klimscha

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Другие публикации лиц с тем же именем

Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data., , , , , , , , и . IEEE Trans. Med. Imaging, 38 (4): 1037-1047 (2019)Correction to: On Orthogonal Projections for Dimension Reduction and Applications in Augmented Target Loss Functions for Learning Problems., , , , , , , , и . J. Math. Imaging Vis., 62 (3): 395 (2020)Correction to "Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT"., , , , , , , и . IEEE Trans. Med. Imaging, 39 (4): 1291 (2020)Identifying and Categorizing Anomalies in Retinal Imaging Data., , , , , , , и . CoRR, (2016)RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge., , , , , , , , , и 22 other автор(ы). IEEE Trans. Medical Imaging, 38 (8): 1858-1874 (2019)Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data., , , , , , , , и . CoRR, (2018)U2-Net: A Bayesian U-Net Model With Epistemic Uncertainty Feedback For Photoreceptor Layer Segmentation In Pathological OCT Scans., , , , , , , и . ISBI, стр. 1441-1445. IEEE, (2019)Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT., , , , , , , и . IEEE Trans. Med. Imaging, 39 (1): 87-98 (2020)Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT., , , , , , , и . CoRR, (2019)On orthogonal projections for dimension reduction and applications in variational loss functions for learning problems., , , , , , , , и . CoRR, (2019)