ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning
Part Of
BHI 2024 - IEEE-EMBS International Conference on Biomedical and Health Informatics, Proceedings
Start Page
1
End Page
8
ISBN (of the container)
979-835035155-2
ISBN
[9798350351552]
Date Issued
2024-11-10
Author(s)
Shun-Cheng Chang
Hsin-Pei Yu
Yi-Hsien Hsieh
Pochuang Wang
Tung-Hung Su
Jia-Horng Kao
DOI
10.1109/BHI62660.2024.10913759
Abstract
Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag.
Event(s)
2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024
SDGs
Publisher
IEEE
Type
conference paper