论文标题
开放式识别乳腺癌治疗
Open-Set Recognition of Breast Cancer Treatments
论文作者
论文摘要
开放集识别通过将测试样本分类为培训或“未知”的已知类别之一，从而概括了分类任务。由于不断发现具有改进治疗的新型癌症药物鸡尾酒，因此可以自然地根据开放式识别问题来表达癌症治疗。由于在培训期间对未知样本进行了建模，因此缺点是由医疗保健开放式学习的先前工作的直接实施引起的。因此，我们重新构架问题方法，并应用了最近现有的高斯混合物变化自动编码器模型，该模型可实现图像数据集的最新结果，以将其用于乳腺癌患者数据。与最近的方法相比，我们不仅获得了更准确，更强大的分类结果，平均F1的平均F1增加了24.5％，而且还以可部署性在临床环境中重新检查开放式识别。
Open-set recognition generalizes a classification task by classifying test samples as one of the known classes from training or "unknown." As novel cancer drug cocktails with improved treatment are continually discovered, predicting cancer treatments can naturally be formulated in terms of an open-set recognition problem. Drawbacks, due to modeling unknown samples during training, arise from straightforward implementations of prior work in healthcare open-set learning. Accordingly, we reframe the problem methodology and apply a recent existing Gaussian mixture variational autoencoder model, which achieves state-of-the-art results for image datasets, to breast cancer patient data. Not only do we obtain more accurate and robust classification results, with a 24.5% average F1 increase compared to a recent method, but we also reexamine open-set recognition in terms of deployability to a clinical setting.
