基于多分辨率特征融合与上下文信息的胃癌复发预测方法

Recurrence prediction of gastric cancer based on multi-resolution feature fusion and context information

胃癌病理图像是胃癌诊断的金标准,然而其复发预测任务面临病灶组织形态特征不显著、多级分辨率特征融合不足、无法有效利用上下文信息等问题。为此,提出了一种基于胃癌病理图像分析的三阶段复发预测方法。在第一阶段,利用自监督学习框架SimCLR对低分辨率下的补丁图像进行训练以降低不同组织图像的耦合度,从而获得解耦后的增强特征。在第二阶段,将获取的低分辨率增强特征与对应高分辨率未增强特征进行融合,实现不同分辨率下的特征互补。在第三阶段,针对补丁图像数量差异较大导致位置编码困难的问题,利用多尺度的局部邻域进行位置编码并利用自注意力机制获得具有上下文信息的特征,随后与卷积神经网络所提取的局部特征进行融合。通过在临床收集的数据上进行评估,与同类方法最佳性能相比,本文所提出的网络模型在准确率、曲线下面积(AUC)指标上取得了最佳性能,分别提高了7.63%、4.51%,证明了该方法对胃癌复发预测的有效性。

Pathological images of gastric cancer serve as the gold standard for diagnosing this malignancy.However,the recurrence prediction task often encounters challenges such as insignificant morphological features of the lesions,insufficient fusion of multi-resolution features,and inability to leverage contextual information effectively.To address these issues,a three-stage recurrence prediction method based on pathological images of gastric cancer is proposed.In the first stage,the self-supervised learning framework SimCLR was adopted to train low-resolution patch images,aiming to diminish the interdependence among diverse tissue images and yield decoupled enhanced features.In the second stage,the obtained low-resolution enhanced features were fused with the corresponding high-resolution unenhanced features to achieve feature complementation across multiple resolutions.In the third stage,to address the position encoding difficulty caused by the large difference in the number of patch images,we performed position encoding based on multi-scale local neighborhoods and employed self-attention mechanism to obtain features with contextual information.The resulting contextual features were further combined with the local features extracted by the convolutional neural network.The evaluation results on clinically collected data showed that,compared with the best performance of traditional methods,the proposed network provided the best accuracy and area under curve(AUC),which were improved by 7.63%and 4.51%,respectively.These results have effectively validated the usefulness of this method in predicting gastric cancer recurrence.