Multi-Scale Transformer Network With Edge-Aware Pre-Training for Cross-Modality MR Image Synthesis

基本事实 人工智能 计算机科学 编码器 模态(人机交互) 自编码 模式识别(心理学) GSM演进的增强数据速率 深度学习 计算机视觉 操作系统
作者
Yonghao Li,Tao Zhou,Kelei He,Yi Zhou,Dinggang Shen
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:42 (11): 3395-3407 被引量:21
标识
DOI:10.1109/tmi.2023.3288001
摘要

Cross-modality magnetic resonance (MR) image synthesis can be used to generate missing modalities from given ones. Existing (supervised learning) methods often require a large number of paired multi-modal data to train an effective synthesis model. However, it is often challenging to obtain sufficient paired data for supervised training. In reality, we often have a small number of paired data while a large number of unpaired data. To take advantage of both paired and unpaired data, in this paper, we propose a Multi-scale Transformer Network (MT-Net) with edge-aware pre-training for cross-modality MR image synthesis. Specifically, an Edge-preserving Masked AutoEncoder (Edge-MAE) is first pre-trained in a self-supervised manner to simultaneously perform 1) image imputation for randomly masked patches in each image and 2) whole edge map estimation, which effectively learns both contextual and structural information. Besides, a novel patch-wise loss is proposed to enhance the performance of Edge-MAE by treating different masked patches differently according to the difficulties of their respective imputations. Based on this proposed pre-training, in the subsequent fine-tuning stage, a Dual-scale Selective Fusion (DSF) module is designed (in our MT-Net) to synthesize missing-modality images by integrating multi-scale features extracted from the encoder of the pre-trained Edge-MAE. Furthermore, this pre-trained encoder is also employed to extract high-level features from the synthesized image and corresponding ground-truth image, which are required to be similar (consistent) in the training. Experimental results show that our MT-Net achieves comparable performance to the competing methods even using 70% of all available paired data. Our code will be released at https://github.com/lyhkevin/MT-Net .
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
末末完成签到 ,获得积分10
1秒前
无为完成签到 ,获得积分10
2秒前
白嫖论文完成签到 ,获得积分10
4秒前
上官若男应助忧伤的步美采纳,获得10
7秒前
科研通AI2S应助科研通管家采纳,获得10
7秒前
10秒前
从心随缘完成签到 ,获得积分10
11秒前
花花发布了新的文献求助10
13秒前
牛奶面包完成签到 ,获得积分10
14秒前
15秒前
岁月如歌完成签到 ,获得积分0
15秒前
18秒前
Li完成签到,获得积分10
20秒前
张琨完成签到 ,获得积分10
20秒前
20秒前
sunnyqqz完成签到,获得积分10
23秒前
热情的乘风完成签到,获得积分20
23秒前
25秒前
霍凡白完成签到,获得积分10
26秒前
27秒前
Feng发布了新的文献求助20
28秒前
怕孤单的若颜完成签到 ,获得积分10
30秒前
31秒前
ruochenzu发布了新的文献求助10
34秒前
zhongu发布了新的文献求助10
38秒前
阳光彩虹小白马完成签到 ,获得积分10
38秒前
Feng完成签到,获得积分10
40秒前
花花完成签到,获得积分10
42秒前
45秒前
量子星尘发布了新的文献求助10
47秒前
杨一完成签到 ,获得积分10
50秒前
猫猫头完成签到 ,获得积分10
51秒前
53秒前
56秒前
忒寒碜完成签到,获得积分10
1分钟前
1分钟前
XU博士完成签到,获得积分10
1分钟前
哭泣青烟完成签到 ,获得积分10
1分钟前
roundtree完成签到 ,获得积分0
1分钟前
等待谷南完成签到,获得积分10
1分钟前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
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 4038039
求助须知:如何正确求助?哪些是违规求助? 3575756
关于积分的说明 11373782
捐赠科研通 3305574
什么是DOI,文献DOI怎么找? 1819239
邀请新用户注册赠送积分活动 892655
科研通“疑难数据库(出版商)”最低求助积分说明 815022