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Abstract：
Prostate cancer is one of the most common and fatal diseases among men, and its early diagnosis can have a significant impact on the treatment process and prevent mortality. Since it does not have apparent clinical symptoms in the early stages, it is difficult to diagnose. In addition, the disagreement of experts in the analysis of magnetic resonance images is also a significant challenge. In recent years, various research has shown that deep learning, especially convolutional neural networks, has appeared successfully in machine vision (especially in medical image analysis). In this research, a deep learning approach was used on multi-parameter magnetic resonance images, and the synergistic effect of clinical and pathological data on the accuracy of the model was investigated. The data were collected from Trita Hospital in Tehran, which included 343 patients (data augmentation and learning transfer methods were used during the process). In the designed model, four different types of images are analyzed with four separate ResNet50 deep convolutional networks, and their extracted features are transferred to a fully connected neural network and combined with clinical and pathological features. In the model without clinical and pathological data, the maximum accuracy reached 88%, but by adding these data, the accuracy increased to 96%, which shows the significant impact of clinical and pathological data on the accuracy of diagnosis.
摘要：
前列腺癌是男性中最常见和最致命的疾病之一，且其早期诊断可对治疗过程产生重大影响，预防死亡。由于它在早期没有明显的临床症状，很难诊断。此外,专家在分析磁共振图像方面的分歧也是一个重大挑战。近年来,各种研究表明，深度学习，尤其是卷积神经网络，已经成功地出现在机器视觉中(特别是在医学图像分析中)。在这项研究中,在多参数磁共振图像上使用了一种深度学习方法，研究了临床和病理数据对模型准确性的协同作用。数据是从德黑兰的Trita医院收集的，其中包括343例患者(在该过程中使用了数据增强和学习迁移方法).在设计的模型中，使用四个独立的ResNet50深度卷积网络分析了四种不同类型的图像，并将其提取的特征转移到完全连接的神经网络，并与临床和病理特征相结合。在没有临床和病理数据的模型中，最高准确率达到88%,但是通过添加这些数据，准确度提高到96%，临床和病理资料对诊断的准确性有显著影响。
