Related Experiment Video For cholangiocarcinoma
Updated: Jul 17, 2025

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment
Published on: March 25, 2020
A histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion
Hui Zhou1, Jingyan Li2, Jue Huang1
1Department of Network Engineering, College of Computer and Software, Nanjing Vocational University of Industry Technology, Nanjing, China.
Abstract:
Histopathological image analysis plays an important role in the diagnosis and treatment of cholangiocarcinoma. This time-consuming and complex process is currently performed manually by pathologists. To reduce the burden on pathologists, this paper proposes a histopathological image classification method for cholangiocarcinoma based on spatial-channel feature fusion convolutional neural networks. Specifically, the proposed model consists of a spatial branch and a channel branch. In the spatial branch, residual structural blocks are used to extract deep spatial features. In the channel branch, a multi-scale feature extraction module and some multi-level feature extraction modules are designed to extract channel features in order to increase the representational ability of the model. The experimental results of the Multidimensional Choledoch Database show that the proposed method performs better than other classical CNN classification methods.
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