Vuong Pham, Hai Nguyen, Bao Pham, Thien Nguyen and Hien Nguyen*
Background: Computer vision in general and semantic segmentation has experienced many achievements in recent years. Consequently, the emergence of medical imaging has provided new opportunities for conducting artificial intelligence research. Since cancer is the second-leading cause of death in the world, early-stage diagnosis is an essential process that directly slows down the development speed of cancer.
Methods: Deep neural network-based methods are anticipated to reduce diagnosis time for pathologists.
Results: In this research paper, an approach to liver tumor identification based on two types of medical images has been presented: computed tomography scans and whole-slide. It is constructed based on the improvement of U-Net and GLNet architectures. It also includes sub-modules that are combined with segmentation models to boost up the overall performance during inference phases.
Conclusion: Based on the experimental results, the proposed unified framework has been emerging to be ready-to-use in the production environment.
Tumor Segmentation, Radiology, Histopathology, Neural Networks, Framework, Deep Learning
Faculty of Information Technology, Sai Gon University, Ho Chi Minh City, Faculty of Software Engineering, University of Information Technology, Ho Chi Minh City, Faculty of Information Technology, Sai Gon University, Ho Chi Minh City, Banking University of Ho Chi Minh City, Ho Chi Minh City, Faculty of Computer Science, University of Information Technology, Ho Chi Minh City, Vietnam, eVietnam National University, Ho Chi Minh City