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Improving Splenomegaly Segmentation by Learning from Heterogeneous Multi-Source Labels
Splenomegaly segmentation on computed tomography (CT) abdomen anatomical scans is essential for identifying spleen biomarkers and has applications for quantitative assessment in patients with liver and spleen disease. Deep convolutional neural network automated segmentation has shown promising perfo...
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| Pubblicato in: | Proc SPIE Int Soc Opt Eng |
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| Autori principali: | , , , , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2019
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6874226/ https://ncbi.nlm.nih.gov/pubmed/31762532 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1117/12.2512842 |
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