Generative Adversarial Network With Robust Discriminator Through Multi-Task Learning for Low-Dose CT Denoising

鉴别器 计算机科学 对抗制 生成对抗网络 人工智能 任务(项目管理) 降噪 图像去噪 计算机视觉 深度学习 模式识别(心理学) 机器学习 工程类 探测器 电信 系统工程
作者
Sunggu Kyung,Jongjun Won,Seongyong Pak,Sunwoo Kim,Sangyoon Lee,Kanggil Park,Gil-Sun Hong,Namkug Kim
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:44 (1): 499-518 被引量:13
标识
DOI:10.1109/tmi.2024.3449647
摘要

Reducing the dose of radiation in computed tomography (CT) is vital to decreasing secondary cancer risk. However, the use of low-dose CT (LDCT) images is accompanied by increased noise that can negatively impact diagnoses. Although numerous deep learning algorithms have been developed for LDCT denoising, several challenges persist, including the visual incongruence experienced by radiologists, unsatisfactory performances across various metrics, and insufficient exploration of the networks' robustness in other CT domains. To address such issues, this study proposes three novel accretions. First, we propose a generative adversarial network (GAN) with a robust discriminator through multi-task learning that simultaneously performs three vision tasks: restoration, image-level, and pixel-level decisions. The more multi-tasks that are performed, the better the denoising performance of the generator, which means multi-task learning enables the discriminator to provide more meaningful feedback to the generator. Second, two regulatory mechanisms, restoration consistency (RC) and non-difference suppression (NDS), are introduced to improve the discriminator's representation capabilities. These mechanisms eliminate irrelevant regions and compare the discriminator's results from the input and restoration, thus facilitating effective GAN training. Lastly, we incorporate residual fast Fourier transforms with convolution (Res-FFT-Conv) blocks into the generator to utilize both frequency and spatial representations. This approach provides mixed receptive fields by using spatial (or local), spectral (or global), and residual connections. Our model was evaluated using various pixel- and feature-space metrics in two denoising tasks. Additionally, we conducted visual scoring with radiologists. The results indicate superior performance in both quantitative and qualitative measures compared to state-of-the-art denoising techniques.
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
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
852应助panpan采纳,获得10
刚刚
windfly完成签到 ,获得积分10
1秒前
阿曼尼完成签到 ,获得积分10
1秒前
柚橘完成签到,获得积分10
1秒前
文献小白完成签到 ,获得积分10
1秒前
婆婆丁完成签到,获得积分10
4秒前
theinu发布了新的文献求助30
4秒前
vergil完成签到,获得积分10
7秒前
梅西完成签到 ,获得积分0
8秒前
纳兰嫣然完成签到,获得积分10
9秒前
11秒前
小北完成签到,获得积分20
11秒前
斯文败类应助程程采纳,获得10
12秒前
烟花应助小密没有秘密采纳,获得10
14秒前
mu完成签到,获得积分10
15秒前
科研通AI2S应助小狗邮递员采纳,获得10
16秒前
zest完成签到,获得积分10
16秒前
慕青应助脑子用去发泡了采纳,获得10
16秒前
未晚完成签到,获得积分10
17秒前
活力半蕾完成签到,获得积分10
18秒前
richestchen完成签到,获得积分10
22秒前
23秒前
852应助科研通管家采纳,获得10
23秒前
23秒前
赘婿应助科研通管家采纳,获得10
24秒前
CodeCraft应助科研通管家采纳,获得10
24秒前
Akim应助科研通管家采纳,获得10
24秒前
24秒前
乐乐应助科研通管家采纳,获得10
24秒前
大个应助科研通管家采纳,获得10
24秒前
24秒前
英俊的铭应助科研通管家采纳,获得10
24秒前
充电宝应助科研通管家采纳,获得10
24秒前
今后应助科研通管家采纳,获得10
24秒前
田様应助科研通管家采纳,获得10
24秒前
酷波er应助科研通管家采纳,获得10
24秒前
25秒前
Hello应助科研通管家采纳,获得30
25秒前
Copyright应助科研通管家采纳,获得10
25秒前
25秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Development of a Bridge Weigh-In-Motion System: A technology to convert the bridge response to the passage of traffic into data on vehicle configurations, speeds, times of travel and weights 1000
Molecular Mechanisms of Photosynthesis, 4th Edition 1000
Organic Reactions, Volume 116 1000
Current concepts in cutaneous toxicity : proceedings of the Fourth Conference on Cutaneous Toxicity, Washington, D.C., May 9-11, 1979 1000
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7265723
求助须知:如何正确求助?哪些是违规求助? 8886631
关于积分的说明 18782521
捐赠科研通 6943236
什么是DOI,文献DOI怎么找? 3202974
关于科研通互助平台的介绍 2376085
邀请新用户注册赠送积分活动 2178894