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Regression Forest-Based Atlas Localization and Direction Specific Atlas Generation for Pancreas Segmentation.

, , , , , , , , и . MICCAI (2), том 9901 из Lecture Notes in Computer Science, стр. 556-563. (2016)

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3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation., , , , , , , , и . DLMIA/ML-CDS@MICCAI, том 10553 из Lecture Notes in Computer Science, стр. 222-230. Springer, (2017)Regression Forest-Based Atlas Localization and Direction Specific Atlas Generation for Pancreas Segmentation., , , , , , , , и . MICCAI (2), том 9901 из Lecture Notes in Computer Science, стр. 556-563. (2016)Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning., , , , , , , , , и 2 other автор(ы). DART/DCL@MICCAI, том 12444 из Lecture Notes in Computer Science, стр. 192-200. Springer, (2020)Towards Automated Colonoscopy Diagnosis: Binary Polyp Size Estimation via Unsupervised Depth Learning., , , , , , , и . MICCAI (2), том 11071 из Lecture Notes in Computer Science, стр. 611-619. Springer, (2018)Graph Cuts Loss to Boost Model Accuracy and Generalizability for Medical Image Segmentation., , и . ICCVW, стр. 3297-3306. IEEE, (2021)Position-based adjustment of landmark-based correspondence finding in electromagnetic sensor-based colonoscope tracking method., , , , , , , , и . Medical Imaging: Image-Guided Procedures, том 9786 из SPIE Proceedings, стр. 97861D. SPIE, (2016)Positive-gradient-weighted object activation mapping: visual explanation of object detector towards precise colorectal-polyp localisation., , , , , и . Int. J. Comput. Assist. Radiol. Surg., 17 (11): 2051-2063 (2022)Gaussian affinity and GIoU-based loss for perforation detection and localization from colonoscopy videos., , , , , , , , и . Int. J. Comput. Assist. Radiol. Surg., 18 (5): 795-805 (мая 2023)Robust colonoscope tracking method for colon deformations utilizing coarse-to-fine correspondence findings., , , , , , , , и . Int. J. Comput. Assist. Radiol. Surg., 12 (1): 39-50 (2017)Unsupervised segmentation of COVID-19 infected lung clinical CT volumes using image inpainting and representation learning., , , , , , , , , и 2 other автор(ы). Medical Imaging: Image Processing, том 11596 из SPIE Proceedings, SPIE, (2021)