基于多任务和注意力的胰腺癌全切片图像多组织分割模型

Multi-tissue segmentation model of whole slide image of pancreatic cancer based on multi task and attention mechanism

在全切片中分割不同组织对胰腺癌的诊疗十分重要,但目前面临内容复杂、样本偏少、样本异质性高等挑战。本文研究了胰腺癌病理切片八种类别的组织分割,通过引入注意力机制并设计分层共享的多任务结构,利用相关辅助任务显著提升模型性能。本文模型在上海长海医院的数据集上进行训练与测试,并在TCGA公开数据集上进行外部验证,在内部测试集上F1分数均高于0.97,在外部验证集上F1分数均高于0.92,且泛化性能显著优于基线方法。实验表明,本文模型可准确分割胰腺癌全切片图像中的不同组织,为临床诊断提供可靠依据。

Accurate segmentation of whole slide images is of great significance for the diagnosis of pancreatic cancer. However, developing an automatic model is challenging due to the complex content, limited samples, and high sample heterogeneity of pathological images. This paper presented a multi-tissue segmentation model for whole slide images of pancreatic cancer. We introduced an attention mechanism in building blocks, and designed a multi-task learning framework as well as proper auxiliary tasks to enhance model performance. The model was trained and tested with the pancreatic cancer pathological image dataset from Shanghai Changhai Hospital. And the data of TCGA, as an external independent validation cohort, was used for external validation. The F1 scores of the model exceeded 0.97 and0.92 in the internal dataset and external dataset, respectively. Moreover, the generalization performance was also better than the baseline method significantly. These results demonstrate that the proposed model can accurately segment eight kinds of tissue regions in whole slide images of pancreatic cancer, which can provide reliable basis for clinical diagnosis.