Abstract
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 used in the production environment.
Keywords:
Tumor segmentation, radiology, histopathology, neural networks, framework, deep learning.
Graphical Abstract
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