Knowledge tensor embedding framework with association enhancement for breast ultrasound diagnosis of limited labeled samples

In the AI diagnosis of breast cancer, instead of ultrasound images from non-standard acquisition process, the Breast Image Reporting and Data System (BI-RADS) reports are widely accepted as the input data since it can give standardized descriptions for the breast ultrasound samples. The BI-RADS reports are usually stored as the format of Knowledge Graph (KG) due to the flexibility, and the KG embedding is a common procedure for the AI analysis on BI-RADS data. However, since most existing embedding methods are based on the local connections in KG, in the situation of limited labeled samples, there is a clear need for embedding based diagnosis method which is capable of representing the global interactions among all entities/relations and associating the labeled/unlabeled samples. To diagnose the breast ultrasound samples with limited labels, in this paper we propose an efficient framework Knowledge Tensor Embedding with Association Enhancement Diagnosis (KTEAED), which adopts tensor decomposition into the embedding to achieve the global representation of KG entities/relations, and introduces the association enhancement strategy to prompt the similarities between embeddings of labeled/unlabeled samples. The embedding vectors are then utilized to diagnose the clinical outcomes of samples by predicting their links to outcomes entities. Through extensive experiments on BI-RADS data with different fractions of labels and ablation studies, our KTEAED displays promising performance in the situations of various fractions of labels. In summary, our framework demonstrates a clear advantage of tackling limited labeled samples of BI-RADS reports in the breast ultrasound diagnosis. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The proposed framework KTEAED demonstrates promising performance in diagnosing breast ultrasound samples with limited labels, by achieving global representation of KG entities/relations and enhancing associations to predict clinical outcomes.