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Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods
We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading. Our method only requires monocular endoscopic videos and a multi-view stereo method, e. g., structure from motion,...
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| Publicado no: | IEEE Trans Med Imaging |
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| Main Authors: | , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7289272/ https://ncbi.nlm.nih.gov/pubmed/31689184 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TMI.2019.2950936 |
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