WET-UNet: Wavelet integrated efficient transformer networks for nasopharyngeal carcinoma tumor segmentation

分割 计算机科学 人工智能 编码器 深度学习 图像分割 小波变换 小波 模式识别(心理学) 操作系统
作者
Yan Zeng,Jun Li,Zhe Zhao,Wei Liang,Penghui Zeng,Shao‐Dong Shen,Kun Zhang,Chong Shen
出处
期刊:Science Progress [SAGE Publishing]
卷期号:107 (2) 被引量:1
标识
DOI:10.1177/00368504241232537
摘要

Nasopharyngeal carcinoma is a malignant tumor that occurs in the epithelium and mucosal glands of the nasopharynx, and its pathological type is mostly poorly differentiated squamous cell carcinoma. Since the nasopharynx is located deep in the head and neck, early diagnosis and timely treatment are critical to patient survival. However, nasopharyngeal carcinoma tumors are small in size and vary widely in shape, and it is also a challenge for experienced doctors to delineate tumor contours. In addition, due to the special location of nasopharyngeal carcinoma, complex treatments such as radiotherapy or surgical resection are often required, so accurate pathological diagnosis is also very important for the selection of treatment options. However, the current deep learning segmentation model faces the problems of inaccurate segmentation and unstable segmentation process, which are mainly limited by the accuracy of data sets, fuzzy boundaries, and complex lines. In order to solve these two challenges, this article proposes a hybrid model WET-UNet based on the UNet network as a powerful alternative for nasopharyngeal cancer image segmentation. On the one hand, wavelet transform is integrated into UNet to enhance the lesion boundary information by using low-frequency components to adjust the encoder at low frequencies and optimize the subsequent computational process of the Transformer to improve the accuracy and robustness of image segmentation. On the other hand, the attention mechanism retains the most valuable pixels in the image for us, captures the remote dependencies, and enables the network to learn more representative features to improve the recognition ability of the model. Comparative experiments show that our network structure outperforms other models for nasopharyngeal cancer image segmentation, and we demonstrate the effectiveness of adding two modules to help tumor segmentation. The total data set of this article is 5000, and the ratio of training and verification is 8:2. In the experiment, accuracy = 85.2% and precision = 84.9% can show that our proposed model has good performance in nasopharyngeal cancer image segmentation.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
勤奋冷安完成签到,获得积分10
1秒前
yydsyyd完成签到 ,获得积分0
2秒前
2秒前
perfectxl完成签到,获得积分10
2秒前
汤泽琪发布了新的文献求助10
3秒前
学术垃圾发布了新的文献求助10
3秒前
yu_xie发布了新的文献求助10
3秒前
3秒前
小二郎应助ww采纳,获得10
5秒前
Rondab应助丢手绢采纳,获得10
6秒前
selena完成签到 ,获得积分10
6秒前
愁思忆完成签到 ,获得积分10
7秒前
木木木发布了新的文献求助10
7秒前
8秒前
直率玉米完成签到,获得积分20
10秒前
heyujie发布了新的文献求助30
10秒前
HYT发布了新的文献求助10
11秒前
12秒前
DD应助cyr采纳,获得10
12秒前
13秒前
糖糖发布了新的文献求助10
14秒前
xiaoyeken发布了新的文献求助10
16秒前
16秒前
16秒前
jidong完成签到,获得积分10
17秒前
17秒前
17秒前
18秒前
可靠的冰烟完成签到,获得积分10
19秒前
19秒前
Rw发布了新的文献求助10
23秒前
23秒前
24秒前
24秒前
26秒前
Invictus发布了新的文献求助10
26秒前
大模型应助科研通管家采纳,获得10
26秒前
领导范儿应助科研通管家采纳,获得10
27秒前
小蘑菇应助科研通管家采纳,获得10
27秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3972313
求助须知:如何正确求助?哪些是违规求助? 3516792
关于积分的说明 11184744
捐赠科研通 3252260
什么是DOI,文献DOI怎么找? 1796300
邀请新用户注册赠送积分活动 876339
科研通“疑难数据库(出版商)”最低求助积分说明 805483