BIN: A Bio-Signature Identification Network for Interpretable Liver Cancer Microvascular Invasion Prediction Based on Multi-modal MRIs

Abstract

Microvascular invasion (MVI) is a critical factor that affects the postoperative cure of hepatocellular carcinoma (HCC). Precise preoperative diagnosis of MVI by magnetic resonance imaging (MRI) is crucial for effective treatment of HCC. Compared with traditional methods, deep learning-based MVI diagnostic models have shown significant improvements. However, the black-box nature of deep learning models poses a challenge to their acceptance in medical fields that demand interpretability. To address this issue, this paper proposes an interpretable deep learning model, called Biosignature Identification Network (BIN) based on multi-modal MRI images for the liver cancer MVI prediction task. Inspired by the biological ways to distinguish the species through the biosignatures, our proposed BIN method classifies patients into MVI absence (i.e., Non-MVI or negative) and MVI presence (i.e., positive) by utilizing Non-MVI and MVI biosignatures. The adoption of a transparent decision-making process in BIN ensures interpretability, while the proposed biosignatures overcome the limitations associated with the manual feature extraction. Moreover, a multi-modal MRI based BIN method is also explored to further enhance the diagnostic performance with an attempt to interpretability of multi-modal MRI fusion. Through extensive experiments on the real dataset, it was found that BIN maintains deep model-level performance while providing effective interpretability. Overall, the proposed model offers a promising solution to the challenge of interpreting deep learning-based MVI diagnostic models.