An Efficient Domain Knowledge-Guided Semantic Prediction Framework for Pathological Subtypes on the Basis of...

Accurate prediction of pathological subtypes on radiological images is one of the most important deep learning (DL) tasks for the appropriate selection of clinical treatment. It is challenging for conventional DL models to obtain sufficient pathological labels for training because of the heavy workload, invasive surgery, and knowledge requirements in pathological analysis. However, existing methods based on limited annotations, such as active learning (AL) and semi-supervised learning (SSL), have difficulty in capturing lesion's effective features because of the complicated semantic information of radiologic images. In this article, we introduce an efficient domain knowledge-guided semantic prediction framework that integrates domain knowledge-guided AL and SSL methods. This framework can effectively predict pathological subtypes on the basis of radiologic images with limited pathological annotations via three key modules: 1) the discriminative spatial-semantic feature extraction module captures the spatial-semantic features of lesions as semantic information that can better reflect the semantic relationship and effectively mitigate overfitting risk; 2) the explicit sign-guided anchor attention module measures the multimodal semantic distribution of samples under the guidance of clinical domain knowledge, thus selecting the most representative AL samples for pathological labeling; and 3) the implicit radiomics-guided dual-task entanglement module exploits the inherent constraint relationships between implicit radiomics features (IRFs) and pathological subtypes, facilitating the aggregation of unlabeled data. Experiments have been extensively conducted to evaluate our method in two clinical tasks: the pathological grading prediction in pancreatic neuroendocrine neoplasms (pNENs) and muscular invasiveness prediction in bladder cancer (BCa). The experimental results on both tasks demonstrate that the proposed method consistently outperforms the state-of-the-art approaches by a large margin.