.png){kind=icon}
.png){kind=icon}
Return
Pathologic liver tumor detection using feature aligned multi-scale convolutional network
.png){kind=icon}
PRE
AI
DOI：10.1016/j.artmed.2022.102244.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The detection of the most common type of liver tumor, that is, hepatocellular carcinoma (HCC), is one essential step to liver pathology image analysis. In liver tissue, common cell change phenomena such as apoptosis, necrosis, and steatosis are similar in tumor and benign tissue. Hence, the detection of HCC may fail when the patches covered only limited tissue region without enough neighboring cell structure information. To address this problem, a Feature Aligned Multi-Scale Convolutional Network (FA-MSCN) architecture is proposed in this paper for automatic liver tumor detection based on whole slide images (WSI). The proposed network integrates the features obtained at different magnification levels to improve the detection performance by referencing more neighboring information. The FA-MSCN consists of two parallel convolutional networks in which one would extract high-resolution features and the other would extract low-resolution features by atrous convolution. The low-resolution features then go through central cropping, upsampling, and concatenation with high-resolution features for final classification. The experimental results demonstrated that Multi-Scale Convolutional Network (MSCN) improves the detection performance compared to Single-Scale Convolutional Network (SSCN), and that the FA-MSCN is superior to both SSCN and MSCN, demonstrating on HCC detection.
Keywords:
Convolutional neural network
Liver tumor detection
Multi-scale
Whole slide image
Hepatocellular carcinoma
Journal
IF:
6.2
Papers:
2.5K
Citations:
7.8K