Microvascular invasion (MVI) is an important factor for early recurrence and poor long-term prognosis in patients with hepatocellular carcinoma (HCC) after resection or transplantation. Therefore,it is of great clinical value to evaluate whether MVI exists in patients with HCC before operation. In recent years,deep learning has provided a valuable solution for MVI image diagnosis and evaluation. Nevertheless,due to the difficulties of data annotation and collection,the current researches mostly use computed tomography (CT) or magnetic resonance imaging (MRI) methods to collect single modal sequences in images independently,which lacks the comprehensive application of multimodal sequences in various imaging methods. In order to make more effective use of multimodal data of CT and MRI images and improve diagnosis ef-ficiency under few-shot scenarios,an efficient multimodal montribution aware network is proposed in this paper. The mo-dality grouping convolution and efficient multimodal adaptive weighting module in this network are used to to learn the di-agnostic contribution of each modal information of CT or MRI under complex and diverse MVI representation with little computational cost introduced. The experiment is carried out on the clinical dataset collected by the third-class hospital. Re-sult show that with the support of a small amount of labeled data,our method can achieve better MVI diagnostic perfor-mance than many deep neural networks based on attention mechanism,which provides an effective reference for profession-al doctors' diagnostic analysis.
维普
万方数据