论文标题
使用自动深度学习从MPMRI到MPMRI的前列腺癌恶性检测和定位：靠近临床利用
Prostate Cancer Malignancy Detection and localization from mpMRI using auto-Deep Learning: One Step Closer to Clinical Utilization
论文作者
论文摘要
在过去的几年中，已对MPMRI的恶性前列腺癌患者进行自动诊断。模型解释和域漂移一直是临床利用的主要路障。作为我们以前的工作的扩展，我们在公共队列上培训了一个定制的卷积神经网络，其中有201名患者和感兴趣区域周围的裁剪2D斑块作为输入，将前列腺的2.5D片段用作输入，并在使用Autokeras的模型空间中搜索了最佳模型。对外围区（PZ）和中央腺（CG）分别进行了训练和测试，有效地证明了一些不同的东西，PZ检测器和CG检测器有效地展示了序列中最可疑的切片，希望大大减轻医生的工作量。
Automatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work where we trained a customized convolutional neural network on a public cohort with 201 patients and the cropped 2D patches around the region of interest were used as the input, the cropped 2.5D slices of the prostate glands were used as the input, and the optimal model were searched in the model space using autoKeras. Something different was peripheral zone (PZ) and central gland (CG) were trained and tested separately, the PZ detector and CG detector were demonstrated effectively in highlighting the most suspicious slices out of a sequence, hopefully to greatly ease the workload for the physicians.
