Faculty of Information Technology, Sai Gon University, Ho Chi Minh City, Vietnam.
Faculty of Software Engineering, University of Information Technology, Ho Chi Minh City, Vietnam.
Curr Med Imaging. 2022;19(1):37-45. doi: 10.2174/1573405617666210804151024.
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.
Deep neural network-based methods are anticipated to reduce diagnosis time for pathologists.
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.
Based on the experimental results, the proposed unified framework has been emerging to be used in the production environment.
计算机视觉，尤其是语义分割，近年来取得了许多成果。因此，医学成像的出现为人工智能研究提供了新的机会。由于癌症是世界上第二大致死原因，早期诊断是一个至关重要的过程，它直接减缓了癌症的发展速度。
基于深度神经网络的方法有望减少病理学家的诊断时间。
在本研究论文中，提出了一种基于两种类型的医学图像的肝脏肿瘤识别方法：计算机断层扫描和全切片。它是基于 U-Net 和 GLNet 架构的改进构建的。它还包括与分割模型相结合的子模块，以在推理阶段提高整体性能。
基于实验结果，提出的统一框架已经开始在生产环境中使用。
