High-grade serous ovarian cancer has a high degree of malignancy, and at detection, it is prone to infiltration of surrounding soft tissues, as well as metastasis to the peritoneum and lymph nodes, peritoneal seeding, and distant metastasis. Whether recurrence occurs becomes an important reference for surgical planning and treatment methods for this disease. Current recurrence prediction models do not consider the potential pathological relationships between internal tissues of the entire ovary. They use convolutional neural networks to extract local region features for judgment, but the accuracy is low, and the cost is high. To address this issue, this paper proposes a new lightweight deep learning algorithm model for predicting recurrence of high-grade serous ovarian cancer. The model first uses ghost convolution (Ghost Conv) and coordinate attention (CA) to establish ghost counter residual (SCblock) modules to extract local feature information from images. Then, it captures global information and integrates multi-level information through proposed layered fusion Transformer (STblock) modules to enhance interaction between different layers. The Transformer module unfolds the feature map to compute corresponding region blocks, then folds it back to reduce computational cost. Finally, each STblock module fuses deep and shallow layer depth information and incorporates patient's clinical metadata for recurrence prediction. Experimental results show that compared to the mainstream lightweight mobile visual Transformer (MobileViT) network, the proposed slicer visual Transformer (SlicerViT) network improves accuracy, precision, sensitivity, and F1 score, with only 1/6 of the computational cost and half the parameter count. This research confirms that the proposed algorithm model is more accurate and efficient in predicting recurrence of high-grade serous ovarian cancer. In the future, it can serve as an auxiliary diagnostic technique to improve patient survival rates and facilitate the application of the model in embedded devices.
高级别浆液性卵巢癌恶性程度高，检出时易发生周围软组织浸润、腹腔与淋巴结转移、腹膜种植和远处转移，是否复发成为该疾病手术计划与治疗手段的重要参考依据。目前的复发预测模型未考虑整个卵巢内部组织之间的潜在病理关系，通常使用较为复杂的卷积神经网络提取局部区域特征进行判断，准确率不高且成本开销大。针对此问题，本文提出了一种新的面向高级别浆液性卵巢癌复发预测的轻量级深度算法模型。该模型先使用鬼影卷积（Ghost Conv）和坐标注意力（CA）建立鬼影倒残差模块（SCblock）提取图像的局部特征信息，然后通过提出的分层融合变换器（Transformer）模块（STblock）进行全局信息的捕捉与多层次信息的融合，提升不同层之间的交互能力；Transformer模块则通过先展开特征图使其对应区域块进行计算，再折叠还原，以此降低计算成本开销。最后，将每个STblock模块进行深浅层的深度信息融合，并加入患者的临床元数据共同进行复发预测。实验结果表明，相较于主流的轻量级移动视觉Transformer网络（MobileViT），本文提出的切片视觉Transformer网络（SlicerViT）在准确率、精确率、灵敏度、F1分数上均有提高，计算量仅为其1/6，参数量降低1/2。本文研究证实了所提算法模型在高级别浆液性卵巢癌的复发预测上更加精确高效，未来可作为一种辅助诊断技术提高患者生存率，并有利于将模型应用于嵌入式设备。.
Keywords: Deep and shallow information fusion; Ghost convolution; Hierarchical transformer; High grade serous ovarian cancer.