论文标题
基于注意的多个实例学习肺癌组织微阵列生存预测
Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays
论文作者
论文摘要
基于注意力的多个实例学习（AMIL）算法已被证明在利用Gigapixel全斜面图像（WSIS）方面成功完成了各种不同的计算病理学任务，例如结果预测和癌症的亚型问题。我们通过利用经典的Cox部分可能性作为损失函数，将AMIL方法扩展到生存预测的任务，并将AMIL模型转换为非线性比例危害模型。我们将该模型应用于330名肺癌患者的组织微阵列（TMA）载玻片。结果表明，与已建立的生存预测方法相比，AMIL方法可以从TMA中处理非常少量的组织，并达到类似的C指数性能，该方法接受了高度歧视性临床因素（例如年龄，癌症等级和癌症阶段）的培训。
Attention-based multiple instance learning (AMIL) algorithms have proven to be successful in utilizing gigapixel whole-slide images (WSIs) for a variety of different computational pathology tasks such as outcome prediction and cancer subtyping problems. We extended an AMIL approach to the task of survival prediction by utilizing the classical Cox partial likelihood as a loss function, converting the AMIL model into a nonlinear proportional hazards model. We applied the model to tissue microarray (TMA) slides of 330 lung cancer patients. The results show that AMIL approaches can handle very small amounts of tissue from a TMA and reach similar C-index performance compared to established survival prediction methods trained with highly discriminative clinical factors such as age, cancer grade, and cancer stage
