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CT Male Pelvic Organ Segmentation via Hybrid Loss Network with Incomplete Annotation
Sufficient data with complete annotation is essential for training deep models to perform automatic and accurate segmentation of CT male pelvic organs, especially when such data is with great challenges such as low contrast and large shape variation. However, manual annotation is expensive in terms...
Tallennettuna:
| Julkaisussa: | IEEE Trans Med Imaging |
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| Päätekijät: | , , , , , , |
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
2020
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8195629/ https://ncbi.nlm.nih.gov/pubmed/31940526 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TMI.2020.2966389 |
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