论文标题
Gleason评分预测，使用组织微阵列图像中的深度学习
Gleason Score Prediction using Deep Learning in Tissue Microarray Image
论文作者
论文摘要
前列腺癌（PCA）是世界各地男性最常见的癌症之一。评估PCA病变水平的最准确方法是对染色活检组织的显微镜检查，并估计专家病理学家的组织微阵列（TMA）图像的Gleason评分。但是，病理学家识别大型TMA图像中Gleason分级的细胞和腺模式是耗时的。我们使用GLEASON2019挑战数据集来构建卷积神经网络（CNN）模型，以将TMA图像细分为不同Gleason等级的区域，并根据分级细分来预测Gleason得分。我们使用了预先培训的前列腺分割模型来提高格里森等级分割的准确性。该模型的平均骰子在测试队列中达到了75.6％，在格里森2019年挑战中排名第四，得分为0.778，Cohen的Kappa和F1-Score的得分为0.778。
Prostate cancer (PCa) is one of the most common cancers in men around the world. The most accurate method to evaluate lesion levels of PCa is microscopic inspection of stained biopsy tissue and estimate the Gleason score of tissue microarray (TMA) image by expert pathologists. However, it is time-consuming for pathologists to identify the cellular and glandular patterns for Gleason grading in large TMA images. We used Gleason2019 Challenge dataset to build a convolutional neural network (CNN) model to segment TMA images to regions of different Gleason grades and predict the Gleason score according to the grading segmentation. We used a pre-trained model of prostate segmentation to increase the accuracy of the Gleason grade segmentation. The model achieved a mean Dice of 75.6% on the test cohort and ranked 4th in the Gleason2019 Challenge with a score of 0.778 combined of Cohen's kappa and the f1-score.
