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Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation.

, , , , , , , , , и . MICCAI (2), том 10434 из Lecture Notes in Computer Science, стр. 253-260. Springer, (2017)

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Oktay Tabasaran

Oktay Celebi

Meral Ozan

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Ozan Gökay

 

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Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing., , , , , , , , , и 2 other автор(ы). ECCV (36), том 13696 из Lecture Notes in Computer Science, стр. 1-21. Springer, (2022)Image-and-Spatial Transformer Networks for Structure-Guided Image Registration., , , , и . MICCAI (2), том 11765 из Lecture Notes in Computer Science, стр. 337-345. Springer, (2019)Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling., , , , , , , , , и 2 other автор(ы). MICCAI (2), том 11071 из Lecture Notes in Computer Science, стр. 464-471. Springer, (2018)Learning a Global Descriptor of Cardiac Motion from a Large Cohort of 1000+ Normal Subjects., , , , , , и . FIMH, том 9126 из Lecture Notes in Computer Science, стр. 3-11. Springer, (2015)Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology., , , , , , , , , и 11 other автор(ы). CHI, стр. 444:1-444:22. ACM, (2024)OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions., , и . Medical Image Anal., (2019)Active label cleaning: Improving dataset quality under resource constraints., , , , , , , , , и 2 other автор(ы). CoRR, (2021)TernaryNet: Faster Deep Model Inference without GPUs for Medical 3D Segmentation using Sparse and Binary Convolutions., , и . CoRR, (2018)Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing., , , , , , , , , и 6 other автор(ы). CVPR, стр. 15016-15027. IEEE, (2023)Cardiac MR Segmentation from Undersampled k-space Using Deep Latent Representation Learning., , , , , , , и . MICCAI (1), том 11070 из Lecture Notes in Computer Science, стр. 259-267. Springer, (2018)