scholarly journals Shape and margin-aware lung nodule classification in low-dose CT images via soft activation mapping

2020 ◽  
Vol 60 ◽  
pp. 101628 ◽  
Author(s):  
Yiming Lei ◽  
Yukun Tian ◽  
Hongming Shan ◽  
Junping Zhang ◽  
Ge Wang ◽  
...  
Keyword(s):  
Low Dose ◽  
Lung Nodule ◽  
Ct Images ◽  
Low Dose Ct ◽  
Nodule Classification ◽  
Activation Mapping

LUNGRADS AND UPDATE OF LUNG NODULES SCREENING BY LOW DOSE COMPUTED TOMOGRAPHY

2015 ◽  
pp. 12-19
Author(s):  
Thi Ngoc Ha Hoang ◽  
Trong Khoan Le
Keyword(s):  
Lung Cancer ◽  
Low Dose ◽  
False Negative ◽  
Lung Nodule ◽  
Lung Cancer Mortality ◽  
Smoking History ◽  
Low Dose Ct ◽  
History Of ◽  
Chest X Ray ◽  
Low Dose Computed Tomography

Background: A pulmonary nodule is defined as a rounded or irregular opacity, well or poorly defined, measuring up to 3 cm in diameter. Early detection the malignancy of nodules has a significant role in decreasing the mortality, increasing the survival time and consider as early diagnosis lung cancer. The main risk factors are those of current or former smokers, aged 55 to 74 years with a smoking history of at least 1 pack-day. Low dose CT: screening individuals with high risk of lung cancer by low dose CT scans could reduce lung cancer mortality by 20 percent compared to chest X-ray. Radiation dose has to maximum reduced but respect the rule of ALARA (As Low as Resonably Archivable). LungRADS 2014: Classification of American College of Radiology, LungRADS, is a newly application but showed many advantages in comparison with others classification such as increasing positive predict value (PPV), no result of false negative and cost effectiveness. Key words: LungRADS, screening lung nodule, low dose CT, lung cancer

scholarly journals Disentangled generative adversarial network for low-dose CT

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wenchao Du ◽  
Hu Chen ◽  
Hongyu Yang ◽  
Yi Zhang
Keyword(s):  
Network Architecture ◽  
Low Dose ◽  
Noise Suppression ◽  
State Of The Art ◽  
Visual Quality ◽  
Ct Images ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network ◽  
Suppression Method

AbstractGenerative adversarial network (GAN) has been applied for low-dose CT images to predict normal-dose CT images. However, the undesired artifacts and details bring uncertainty to the clinical diagnosis. In order to improve the visual quality while suppressing the noise, in this paper, we mainly studied the two key components of deep learning based low-dose CT (LDCT) restoration models—network architecture and adversarial loss, and proposed a disentangled noise suppression method based on GAN (DNSGAN) for LDCT. Specifically, a generator network, which contains the noise suppression and structure recovery modules, is proposed. Furthermore, a multi-scaled relativistic adversarial loss is introduced to preserve the finer structures of generated images. Experiments on simulated and real LDCT datasets show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods.

The Gated Recurrent Conditional Generative Adversarial Network (GRC-GAN): application to denoising of low-dose CT images

2021 ◽  
Author(s):  
Mateus Baltazar de Almeida ◽  
Luis F. Alves Pereira ◽  
Tsang Ing Ren ◽  
George D. C. Cavalcanti ◽  
Jan Sijbers
Keyword(s):  
Low Dose ◽  
Ct Images ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network
Sign in / Sign up

Export Citation Format

Share Document