Collaborative Multi-Metadata Fusion to Improve the Classification of Lumbar Disc Herniation

计算机科学 人工智能 特征(语言学) 模式识别(心理学) 卷积神经网络 特征选择 上下文图像分类 计算机辅助诊断 最小边界框 特征提取 Sørensen–骰子系数 分割 图像分割 计算机视觉 图像(数学) 哲学 语言学
作者
Shuyi Lu,Jinhua Liu,Xiaojie Wang,Yuanfeng Zhou
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:42 (12): 3590-3601 被引量:1
标识
DOI:10.1109/tmi.2023.3294248
摘要

Computed tomography (CT) images are the most commonly used radiographic imaging modality for detecting and diagnosing lumbar diseases. Despite many outstanding advances, computer-aided diagnosis (CAD) of lumbar disc disease remains challenging due to the complexity of pathological abnormalities and poor discrimination between different lesions. Therefore, we propose a Collaborative Multi-Metadata Fusion classification network (CMMF-Net) to address these challenges. The network consists of a feature selection model and a classification model. We propose a novel Multi-scale Feature Fusion (MFF) module that can improve the edge learning ability of the network region of interest (ROI) by fusing features of different scales and dimensions. We also propose a new loss function to improve the convergence of the network to the internal and external edges of the intervertebral disc. Subsequently, we use the ROI bounding box from the feature selection model to crop the original image and calculate the distance features matrix. We then concatenate the cropped CT images, multiscale fusion features, and distance feature matrices and input them into the classification network. Next, the model outputs the classification results and the class activation map (CAM). Finally, the CAM of the original image size is returned to the feature selection network during the upsampling process to achieve collaborative model training. Extensive experiments demonstrate the effectiveness of our method. The model achieved 91.32% accuracy in the lumbar spine disease classification task. In the labelled lumbar disc segmentation task, the Dice coefficient reaches 94.39%. The classification accuracy in the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) reaches 91.82%.
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
嘟嘟发布了新的文献求助10
刚刚
1秒前
1秒前
田様应助乐正飞风采纳,获得10
1秒前
555557应助小梦采纳,获得10
1秒前
Ae完成签到,获得积分10
2秒前
2秒前
3秒前
3秒前
susu完成签到,获得积分10
3秒前
3秒前
3秒前
3秒前
3秒前
4秒前
冷酷寒安发布了新的文献求助10
5秒前
落林樾发布了新的文献求助10
6秒前
优雅泡芙发布了新的文献求助20
6秒前
背后友蕊发布了新的文献求助10
6秒前
7秒前
果泥发布了新的文献求助20
8秒前
林子青发布了新的文献求助10
8秒前
无奈行恶应助典雅的俊驰采纳,获得10
8秒前
8秒前
和谐山灵完成签到,获得积分20
9秒前
小鱼儿发布了新的文献求助10
9秒前
10秒前
10秒前
10秒前
Dddd发布了新的文献求助10
10秒前
11秒前
11秒前
科目三应助啊棕采纳,获得10
11秒前
科研通AI2S应助草莓采纳,获得10
12秒前
L91完成签到,获得积分10
12秒前
13秒前
康康星发布了新的文献求助10
13秒前
石若楠发布了新的文献求助10
14秒前
14秒前
小鹿5460发布了新的文献求助30
14秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
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 3974797
求助须知:如何正确求助?哪些是违规求助? 3519250
关于积分的说明 11197623
捐赠科研通 3255405
什么是DOI,文献DOI怎么找? 1797769
邀请新用户注册赠送积分活动 877156
科研通“疑难数据库(出版商)”最低求助积分说明 806202