Paper Info
Title
Automatic Computer-Aided Histopathologic Segmentation for Nasopharyngeal Carcinoma Using Transformer Framework
Abstract
The segmentation of the histopathological whole slide images (WSIs) of nasopharyngeal carcinoma (NPC) plays an essential role in the diagnosis, grading and even prognosis analysis. Due to the huge size of pathological images and the fact that NPC often occurs in the middle and advanced stages, it is still challenging to generate accurate segmentation results automatically. Although many convolutional neural network (CNN) methods had achieved good segmentation performance in many types of images, however, the encoding of global context is insufficient, and it is prone to misjudge the adjacent regions. Meanwhile, the area of NPC pathological image is dense, which means that the image with a tiny size may fall into one category. To overcome this limitation, we apply a transformer-based framework on NPC pathological images that is designed for extracting and encoding global context information. To validate and compare the transformer framework with various CNN-based methods, experiments have been conducted on the clinical dataset collection of NPC. The transformer framework outperformed the state-of-the-art pure CNN-based methods in AUC and recall. Especially, our framework achieved 2.5%–3.5% higher DSC in 5X images and 2.1%–3.2% higher DSC in 10X images than other methods.
Year
DOI
Venue
2022
10.1007/978-3-031-17266-3_14
Computational Mathematics Modeling in Cancer Analysis
Keywords
DocType
Volume
Nasopharyngeal carcinoma, Histopathological whole slide images, Transformer, Segmentation
Conference
13574
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Diao Songhui100.34
Tang Luyu200.34
He Jiahui300.34
Zhao Hanqing400.34
Luo Weiren500.34
Xie Yaoqin600.34
Qin Wenjian700.34