A5056094234- Radiomics and Machine Learning in Medical Imaging
- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
- Lung Cancer Diagnosis and Treatment
- Lung Cancer Treatments and Mutations
- COVID-19 diagnosis using AI
- Face and Expression Recognition
- Inflammatory Biomarkers in Disease Prognosis
- AI in cancer detection
- Face recognition and analysis
- Rough Sets and Fuzzy Logic
- Data Mining Algorithms and Applications
- Advanced Image and Video Retrieval Techniques
- Advanced Computational Techniques and Applications
- RNA modifications and cancer
- Cancer Risks and Factors
- Brain Tumor Detection and Classification
- Image Retrieval and Classification Techniques
- Breast Cancer Treatment Studies
- Geomechanics and Mining Engineering
- Biometric Identification and Security
- Protein Tyrosine Phosphatases
- Head and Neck Cancer Studies
- Colorectal Cancer Treatments and Studies
- Colorectal Cancer Screening and Detection
Huazhong University of Science and Technology
2013-2024
Yanching Institute of Technology
2024
Sichuan University
2017-2022
West China Hospital of Sichuan University
2017-2022
Emory University
2022
Winship Cancer Institute
2022
Chongqing Dazu District People's Hospital
2021
Association for Language Learning
2019
West China Medical Center of Sichuan University
2017
University of Baghdad
2016
Early detection of lung cancer is an effective way to improve the survival rate patients. It a critical step have accurate nodules in computed tomography (CT) images for diagnosis cancer. However, due heterogeneity and complexity surrounding environment, it challenge develop robust nodule method. In this study, we propose two-stage convolutional neural networks (TSCNN) detection. The first stage based on improved U-Net segmentation network establish initial nodules. During stage, order...
Abstract Image retrieval is the process of retrieving images from a database. Certain algorithms have been used for traditional image retrieval. However, such involves certain limitations, as manual annotation, ineffective feature extraction, inability capability to handle complex queries, increased time required, and production less accurate results. To overcome these issues, an effective method proposed in this study. This work intends effectively retrieve using best extraction process. In...
Abstract The present study aimed to determine the correlation between controlling nutritional status (CONUT) and prognosis in resected breast cancer patients. Totally, 861 patients with surgical resection West China Hospital of Sichuan University 2007 2010 were included. relationship CONUT various clinicopathological factors as well was evaluated. results showed that optimal cutoff value for predict 5-year survival 3 had a higher area under ROC curve (AUC) disease free (DFS) overall (OS)...
It is critical to have accurate detection of lung nodules in CT images for the early diagnosis cancer. In order achieve this, it necessary reduce false positive rate detection. Due heterogeneity and their similarity background, difficult distinguish true from numerous candidate nodules. this paper, solve challenging problem, we propose a Multi-Branch Ensemble Learning architecture based on three-dimensional (3D) convolutional neural networks (MBEL-3D-CNN). The method combines three key...
Abstract Background Nm23‐H1 gene has been found to be an inhibitor of tumor metastasis in lung cancer. MicroRNAs (miRNAs) play key roles through multiple signaling pathways. This study explored whether the nm23‐H1 could inhibit invasion and cancer cells by regulating miRNA‐660‐5p targets. Methods Quantitative real‐time PCR (qRT‐PCR) western blots were used measure expression miR‐660‐5p various human cell lines. Cell counting kit‐8 (CCK‐8), wound‐healing transwell assay carried out assess...
The preoperative systemic immune-inflammation index (SII) is correlated with prognosis in several malignancies. aim of this study was to investigate the value SII patients resected breast cancer.A total 784 cancer who underwent surgical resection were consecutively investigated. optimal cutoff evaluated using receiver operating characteristic (ROC) curve. collection clinicopathological and further evaluated.The for prediction survival 514 according ROC curve analysis. A high significantly...
Metric learning has significantly improved machine applications such as face re-identification and image classification using K-Nearest Neighbor (KNN) Support Vector Machine (SVM) classifiers. However, to the best of our knowledge, it not been investigated yet, especially for multimodal biometric recognition problem in immigration, forensic surveillance with uncontrolled ear datasets. Therefore, is interesting very attractive propose a novel framework based on Learning Distance (LDM) via...
Brain tissue segmentation in multi-modal magnetic resonance (MR) images is significant for the clinical diagnosis of brain diseases. Due to blurred boundaries, low contrast, and intricate anatomical relationships between regions, automatic without prior knowledge still challenging. This paper presents a novel 3D fully convolutional network (FCN) segmentation, called APRNet. In this network, we first propose anisotropic pyramidal reversible residual sequence (3DAPC-RRS) module integrate...
Facial expression recognition (FER) is an essential subject of computer vision and human-computer interaction. It has been reported that many factors are closely related to the FER performance such as pose, facial muscle variations, ignored color information in images. In this study, we propose a quaternion capsule neural network (Q-CapsNet) with region attention mechanism for The proposed Q-CapsNet end-to-end deep learning framework, which adopts concept theory Capsule Neural Network...
Deformable image registration (DIR) of lung four-dimensional computed tomography (4DCT) plays a vital role in wide range clinical applications. Most the existing deep learning-based 4DCT DIR methods focus on pairwise which aims to register two images with large deformation. However, temporal continuities deformation fields between phases are ignored. This paper proposes fast and accurate approach that leverages component images.We present Lung-CRNet, an end-to-end convolutional recurrent...