改进卷积神经网络的前列腺癌格里森分级研究

前列腺癌是全球范围内男性最常见的癌症之一,仅次于肺癌.在前列腺癌的诊断过程中最常用的方法是病理学专家通过显微镜对染色活检组织进行观察,得出组织微阵列图像的Gleason评分.在大量的组织微阵列图像下,病理学专家使用Gleason模式对前列腺癌组织微阵列进行评分非常耗时,易受到不同观察者之间主观因素的影响,且可重复性低....

Gleason Grading of Prostate Cancer Based on Improved Convolution Neural Network

Prostate cancer is one of the most common cancers in the world, second only to lung cancer. In the diagnosis of prostate cancer, the most commonly used method is pathological experts to observe the stained biopsy tissue through the microscope, and get the Gleason score of tissue microarray image. In a large number of tissue microarray images, it is very time-consuming for pathological experts to use Gleason pattern to score prostate cancer tissue microarray, which is easily affected by subjective factors among different observers, and has low repeatability. The development of deep learning and computer vision makes the computer-aided diagnosis system of pathology more objective and repeatable. U-Net is the most widely used network in the field of medical image segmentation, which is different from the classifier used in previous studies. A region segmentation model based on improved net network is proposed, which combines the deep and shallow features through dense connection blocks and supervises the features of each scale at the same time. It can reduce the network parameters and improve the calculation efficiency, and verify the effectiveness of the method on the annotated complete dataset.