论文标题
从健康器官的大数据库中学习形状分布：零拍的应用和少数弹药异常检测
Learning shape distributions from large databases of healthy organs: applications to zero-shot and few-shot abnormal pancreas detection
论文作者
论文摘要
我们提出了一种可扩展和数据驱动的方法，以从健康器官的大数据库中学习形状分布。为此，将体积分割掩码嵌入了一个通用的概率形状空间中，该空间通过变异自动编码网络学习。将最终的潜在形状表示被利用以得出Zeroshot和几乎没有射击的方法来检测异常。在1200个健康胰腺形状的大数据库中说明了提出的分配学习方法。下游定性和定量实验是在不同条件患者的224个胰腺测试集上进行的。在零拍摄的配置中，异常的胰腺检测AUC最高可达65.41％，在少数拍摄的配置中达到78.97％，示例少于15个异常示例，优于基于唯一体积的基线方法。
We propose a scalable and data-driven approach to learn shape distributions from large databases of healthy organs. To do so, volumetric segmentation masks are embedded into a common probabilistic shape space that is learned with a variational auto-encoding network. The resulting latent shape representations are leveraged to derive zeroshot and few-shot methods for abnormal shape detection. The proposed distribution learning approach is illustrated on a large database of 1200 healthy pancreas shapes. Downstream qualitative and quantitative experiments are conducted on a separate test set of 224 pancreas from patients with mixed conditions. The abnormal pancreas detection AUC reached up to 65.41% in the zero-shot configuration, and 78.97% in the few-shot configuration with as few as 15 abnormal examples, outperforming a baseline approach based on the sole volume.
