Zhou Hongyu, Tao Haibo, Xue Feiyue, Wang Bin, Jin Huaiping, Li Zhenhui
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.
Yunnan Key Laboratory of Artificial Intelligence, Kunming 650500, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Oct 25;41(5):886-894. doi: 10.7507/1001-5515.202403014.
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.
胃癌的病理图像是诊断这种恶性肿瘤的金标准。然而，复发预测任务常常面临挑战，如病变的形态特征不明显、多分辨率特征融合不足以及无法有效利用上下文信息等。为了解决这些问题，提出了一种基于胃癌病理图像的三阶段复发预测方法。在第一阶段，采用自监督学习框架SimCLR来训练低分辨率切片图像，旨在减少不同组织图像之间的相互依赖并产生解耦的增强特征。在第二阶段，将获得的低分辨率增强特征与相应的高分辨率未增强特征进行融合，以实现多分辨率的特征互补。在第三阶段，为了解决切片图像数量差异较大导致的位置编码困难，我们基于多尺度局部邻域进行位置编码，并采用自注意力机制来获取具有上下文信息的特征。将得到的上下文特征与卷积神经网络提取的局部特征进一步结合。对临床收集数据的评估结果表明，与传统方法的最佳性能相比，所提出的网络提供了最佳的准确率和曲线下面积（AUC），分别提高了7.63%和4.51%。这些结果有效地验证了该方法在预测胃癌复发方面的有效性。
