A5061780917- Medical Imaging Techniques and Applications
- Advanced X-ray and CT Imaging
- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
- Image and Signal Denoising Methods
- Radiation Dose and Imaging
- AI in cancer detection
- Anomaly Detection Techniques and Applications
- Advanced Image Processing Techniques
- Lung Cancer Diagnosis and Treatment
- Machine Learning in Healthcare
- Nuclear Physics and Applications
Tongren University
2019-2023
Chongqing University
2019-2021
Potential risk of X-ray radiation from computed tomography (CT) has been a concern the public. However, simply decreasing dose will degrade quality CT images and compromise diagnostic performance. In this paper, we propose noise learning generative adversarial network coupling with least squares, structural similarity L1 losses for low-dose denoising. our method, distributed in input image is learned by generator then subtracted to generate final denoised version. The are penalized squares...
Abstract Coronavirus disease 2019 (COVID-19) has brought huge losses to the world, and it remains a great threat public health. X-ray computed tomography (CT) plays central role in management of COVID-19. Traditional diagnosis with pulmonary CT images is time-consuming error-prone, which could not meet need for precise rapid COVID-19 screening. Nowadays, deep learning (DL) been successfully applied image analysis, assists radiologists workflow scheduling treatment planning patients methods...
Abstract Segmenting lesion regions of Coronavirus Disease 2019 (COVID-19) from computed tomography (CT) images is a challenge owing to COVID-19 lesions characterized by high variation, low contrast between infection and around normal tissues, blurred boundaries infections. Moreover, shortage available CT dataset hinders deep learning techniques applying tackling COVID-19. To address these issues, we propose learning-based approach known as PPM-Unet segmenting images. Our method improves an...
BACKGROUND: Chest CT scan is an effective way to detect and diagnose COVID-19 infection. However, features of infection in chest images are very complex heterogeneous, which make segmentation lesions from quite challenging. OBJECTIVE: To overcome this challenge, study proposes tests end-to-end deep learning method called dual attention fusion UNet (DAF-UNet). METHODS: The proposed DAF-UNet improves the typical into advanced architecture. dense-connected convolution adopted replace operation....
X-Ray computed tomography (CT) is one of the most popular imaging modality in medical image analysis for clinical application. Meanwhile, potential risk radiation dose to patients has attracted public attention. Over past decades, extensive efforts have been made developing low-dose CT. However, reduction may result increased noise and artifacts, which can significantly compromise quality deteriorate diagnostic performance. Hence, restoring CT from improving performance a challenging vast...