基于Ghost卷积的高级别浆液性卵巢癌复发预测方法

Ghost Convolution Based Prediction Method for Recurrence of High Grade Serous Ovarian Cancer

高级别浆液性卵巢癌是一种恶性肿瘤疾病,进行术前复发预测能帮助临床医生为患者提供个性化治疗方案,降低病人的死亡率。因该疾病的医学数据较少且难以获取,导致其深度学习模型难以得到充分的训练,复发预测准确率有待提高。针对此问题,本文设计了一种改进的低参数残差网络TGE-ResNet34,以ResNet34为主干网络,将传统卷积模块用Ghost卷积代替,完成病灶区特征的提取,降低模型的参数量,在2个Ghost卷积之间融入ECA(Efficient Channel Attention)注意力机制,抑制无用特征提取的干扰,最后通过5折交叉验证模型,避免数据随机划分的偶然性。实验结果表明,改进设计的TGE-ResNet34网络准确率为96.01%,相比原基线网络准确率提高4.52个百分点,参数量减少15.98 M。

High grade serous ovarian cancer is a malignant tumor disease,and preoperative recurrence prediction can help clini⁃cal doctors provide personalized treatment plans for patients and reduce the mortality rate.Due to the less and difficult-to-obtain medical data of this disease,its deep learning model is difficult to obtain sufficient training,and the accuracy of recurrence pre⁃diction needs to be improved.To address this issue,this article proposes an improved low-parameter residual network TGEResNet34,which uses ResNet34 as the backbone network and replaces traditional convolution modules with Ghost convolutions to extract lesion area features and reduce the model’s parameter volume.The ECA(Efficient Channel Attention)attention mechanism is incorporated between two Ghost convolutions to suppress interference from useless feature extraction.Finally,the model is evaluated through a five-fold cross-validation to avoid the randomness of data partitioning.The experimental results show that the accuracy of the improved TGE-ResNet34 network is 96.01%,which is 4.52 percentage points higher than the origi⁃nal baseline network’s accuracy and reduces the parameter volume by 15.98 M.