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MDT-Net: Multi-Domain Transfer by Perceptual Supervision for Unpaired Images in OCT Scan.

, , , , , and . ISBI, page 1-5. IEEE, (2023)

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MDT-Net: Multi-domain Transfer by Perceptual Supervision for Unpaired Images in OCT Scan., , , , and . CoRR, (2022)SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs., and . CoRR, (2023)Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis., , , , , , and . MICCAI (4), volume 11767 of Lecture Notes in Computer Science, page 384-393. Springer, (2019)A Classification-Enhanced Vote Accumulation Scheme for Detecting Colonic Polyps., , and . Abdominal Imaging, volume 8198 of Lecture Notes in Computer Science, page 53-62. Springer, (2013)Noisy Iris Verification: A Modified Version of Local Intensity Variation Method., , and . ICB, volume 5558 of Lecture Notes in Computer Science, page 1150-1159. Springer, (2009)Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation., , , , , and . CoRR, (2019)Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information., , and . IEEE Trans. Medical Imaging, 35 (2): 630-644 (2016)Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?, , , , , , and . CoRR, (2017)Ensuring High-Quality Colonoscopy by Reducing Polyp Miss-Rates.. Arizona State University, Tempe, USA, (2015)base-search.net (ftarizonastateun:item:29977).On the Necessity of Fine-Tuned Convolutional Neural Networks for Medical Imaging., , , , , , and . Deep Learning and Convolutional Neural Networks for Medical Image Computing, Springer, (2017)