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Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease.

, , , , , и . Int. J. Biomedical Imaging, (2016)

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Thor Waldstein

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Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data., , , , , , , , и . CoRR, (2018)Spatio-Temporal Signatures to Predict Retinal Disease Recurrence., , , , , , , и . IPMI, том 9123 из Lecture Notes in Computer Science, стр. 152-163. Springer, (2015)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 . CoRR, (2019)f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks., , , , и . Medical Image Anal., (2019)A novel benchmark model for intelligent annotation of spectral-domain optical coherence tomography scans using the example of cyst annotation., , , , , , , , , и . Comput. Methods Programs Biomed., (2016)Improve synthetic retinal OCT images with present of pathologies and textural information., , , , , , , и . Medical Imaging: Image Processing, том 9784 из SPIE Proceedings, стр. 97843V. SPIE, (2016)Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT., , , , , , , и . IEEE Trans. Med. Imaging, 39 (1): 87-98 (2020)Using Cyclegans for Effectively Reducing Image Variability Across OCT Devices and Improving Retinal Fluid Segmentation., , , , , , , и . ISBI, стр. 605-609. IEEE, (2019)Geodesic denoising for optical coherence tomography images., , , , , , , и . Medical Imaging: Image Processing, том 9784 из SPIE Proceedings, стр. 97840K. SPIE, (2016)