标题： 基于进化算法的CNN前列腺癌检测微调 显示英文标题

标题： Evolution-based Fine-tuning of CNNs for Prostate Cancer Detection

摘要： 卷积神经网络（CNNs）已被用于前列腺癌的自动检测，其中接收器操作特征（ROC）曲线下的面积（AUC）通常用作性能指标。 由于AUC不可微，常见的做法是使用基于其他性能指标的损失函数对CNN进行训练，例如交叉熵，并监控AUC以选择最佳模型。 在本工作中，我们提出使用遗传算法对训练好的CNN进行微调，以实现更高的AUC。 我们的数据集包含6通道的扩散加权MRI前列腺切片。 在2,955个训练、1,417个验证和1,334个测试切片的队列中，我们达到了测试AUC为0.773；与基础CNN模型相比提高了9.3%。 显示英文摘要

摘要： Convolutional Neural Networks (CNNs) have been used for automated detection of prostate cancer where Area Under Receiver Operating Characteristic (ROC) curve (AUC) is usually used as the performance metric. Given that AUC is not differentiable, common practice is to train the CNN using a loss functions based on other performance metrics such as cross entropy and monitoring AUC to select the best model. In this work, we propose to fine-tune a trained CNN for prostate cancer detection using a Genetic Algorithm to achieve a higher AUC. Our dataset contained 6-channel Diffusion-Weighted MRI slices of prostate. On a cohort of 2,955 training, 1,417 validation, and 1,334 test slices, we reached test AUC of 0.773; a 9.3% improvement compared to the base CNN model.