Robust engineering-based unified biomedical imaging framework for liver tumor segmentation.

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.