论文笔记（四）《Adversarial Networks for the Detection of Aggressive Prostate Cancer》
0 Abstract 我们提出利用一个对抗性网络来区分专家和生成的注释，以训练对好的分割进行了学习参数化，因此我们假设该方法对于复杂结构的小型数据集上的分割任务具有独特的优势。在我们的实验中也是如此:我们学习利用 152 名患者的 MRI 图像来分割侵袭性前列腺癌，结果表明，在检测侵袭性前列腺癌的灵敏度和骰子得分方面，提出的方案优于行业标准。所取得的相对增益在小数据集中表现得尤为明显。 1 Int
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相关文章
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