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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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| 發表在: | Proc SPIE Int Soc Opt Eng |
|---|---|
| Main Authors: | , , , , , , , , |
| 格式: | Artigo |
| 語言: | Inglês |
| 出版: |
2019
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| 主題: | |
| 在線閱讀: | 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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