A5003777802- Radiomics and Machine Learning in Medical Imaging
- Lung Cancer Diagnosis and Treatment
- COVID-19 diagnosis using AI
- Functional Brain Connectivity Studies
- AI in cancer detection
- Advanced MRI Techniques and Applications
- Advanced Neuroimaging Techniques and Applications
- Advanced X-ray and CT Imaging
- Medical Imaging Techniques and Applications
- Image Retrieval and Classification Techniques
- Image Processing Techniques and Applications
- Face and Expression Recognition
- Cell Image Analysis Techniques
- Sparse and Compressive Sensing Techniques
- Colorectal Cancer Screening and Detection
- Neural dynamics and brain function
- MRI in cancer diagnosis
- EEG and Brain-Computer Interfaces
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Medical Image Segmentation Techniques
- Inhalation and Respiratory Drug Delivery
- Voice and Speech Disorders
- Tracheal and airway disorders
- Parkinson's Disease Mechanisms and Treatments
- Acute Ischemic Stroke Management
Northeastern University
2016-2025
Central Hospital Affiliated to Shenyang Medical College
2024-2025
First Affiliated Hospital of GuangXi Medical University
2024
Guangxi Medical University
2024
First Hospital of Jilin University
2024
Jilin University
2024
Universidad del Noreste
2020-2023
University of Chicago
2023
Neusoft (China)
2018-2020
Ministry of Education of the People's Republic of China
2014-2020
BACKGROUND: The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social healthcare system. Rapid detection COVID-19 cases significant step to fight against this virus as well release off the OBJECTIVE: One critical factors behind rapid spread pandemic lengthy clinical testing time. imaging tool, such Chest X-ray (CXR), can speed up...
In recent years, researches are concentrating on the effectiveness of Transfer Learning (TL) and Ensemble (EL) techniques in cervical histopathology image analysis. However, there have been very few investigations that described stages differentiation histopathological images. Therefore, this article, we propose an Ensembled (ETL) framework to classify well, moderate poorly differentiated First all, developed Inception-V3, Xception, VGG-16, Resnet-50 based TL structures. Then, enhance...
Cervical cancer is one of the most common and deadliest cancers among women. Despite that, this entirely treatable if it detected at a precancerous stage. Pap smear test extensively performed screening method for early detection cervical cancer. However, hand-operated approach suffers from high false-positive result because human errors. To improve accuracy manual practice, computer-aided diagnosis methods based on deep learning developed widely to segment classify cytology images...
CT screening has been proven to be effective for diagnosing lung cancer at its early manifestation in the form of pulmonary nodules, thus decreasing mortality. However, exponential increase image data makes their accurate assessment a very challenging task given that number radiologists is limited and they have overworked. Recently, numerous methods, especially ones based on deep learning with convolutional neural network (CNN), developed automatically detect classify nodules medical images....
Lung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and treatment of lung diseases. Abnormal lungs mainly include parenchyma with commonalities on CT images across subjects, diseases scanners, lesions presenting various appearances. Segmentation can help locate analyze neighboring lesions, but is not well studied in framework machine learning.We proposed segment using convolutional neural network (CNN) model. To...
Various deep convolutional neural networks (CNNs) have been used to distinguish between benign and malignant pulmonary nodules using CT images. However, single learner usually presents unsatisfied performance due limited hypothesis space, or falling into local minima, wrong selection of space. To tackle these issues, we propose build ensemble learners through fusing multiple CNN for classification. image patches 743 are extracted from LIDC-IDRI database utilized. First, eight with different...
Breast cancer, if diagnosed and treated early, has a better chance of surviving. Many studies have shown that larger number ultrasound images are generated every day, the radiologists able to analyze this medical data is very limited. This often results in misclassification breast lesions, resulting high false-positive rate. In article, we propose computer-aided diagnosis (CAD) system can automatically generate an optimized algorithm. To train machine learning, employ 13 features out 185...
Subclinical depression (ScD) is a prevalent condition associated with relatively mild depressive states, and it poses high risk of developing into major disorder (MDD). However, the neural pathology ScD still largely unknown. Identifying spontaneous activity involved in may help clarify factors for MDD explore treatment strategies stages depression.A total 34 subjects 40 age-, sex-, education-matched healthy controls were screened from 1105 college students. The amplitude low-frequency...
Microorganisms play a great role in ecosystem, wastewater treatment, monitoring of environmental changes, and decomposition waste materials. However, some them are harmful to humans animals such as tuberculosis bacteria plasmodium. In course, it is important identify, track, analyze, consider the beneficial side get rid negative effects microorganisms using fast, accurate, reliable methods. recent decades, image analysis techniques have been used address drawbacks manual traditional...
Subclinical depression (SD) has been considered as the precursor to major depressive disorder. Accurate prediction of SD and identification its etiological origin are urgent. Bursts within lateral habenula (LHb) drive in rats, but whether dysfunctional LHb is associated with human unknown. Here we develop connectome-based biomarkers which predict identify brain regions connections. T1 weighted images resting-state functional MRI (fMRI) data were collected from 34 subjects 40 healthy controls...
Chronic obstructive pulmonary disease (COPD) is associated with morphologic abnormalities of airways various patterns and severities. However, the way effectively representing these lacking whether enable to distinguish COPD from healthy controls unknown. We propose use deep convolutional neural network (CNN) assess 3D lung airway tree perspective computer vision, thereby constructing models identifying COPD. After extracting trees CT images, snapshots their visualizations are obtained...
Objective. Emphysema is characterized by the destruction and permanent enlargement of alveoli in lung. According to visual CT appearance, emphysema can be divided into three subtypes: centrilobular (CLE), panlobular (PLE), paraseptal (PSE). Automating classification help precisely determine patterns lung provide a quantitative evaluation.Approach. We propose vision transformer (ViT) model classify subtypes via images. First, large patches (61×61) are cropped from images which contain area...
To recognize the epidermal growth factor receptor (EGFR) gene mutation status in lung adenocarcinoma (LADC) has become a prerequisite of deciding whether EGFR-tyrosine kinase inhibitor (EGFR-TKI) medicine can be used. Polymerase chain reaction assay or sequencing is for measuring EGFR status, however, tissue samples by surgery biopsy are required. We propose to develop deep learning models using radiomics features extracted from non-invasive CT images. Preoperative images, and clinical data...
Objective.Diabetic retinopathy (DR) grading is primarily performed by assessing fundus images. Many types of lesions, such as microaneurysms, hemorrhages, and soft exudates, are available simultaneously in a single image. However, their sizes may be small, making it difficult to differentiate adjacent DR grades even using deep convolutional neural networks (CNNs). Recently, vision transformer has shown comparable or superior performance CNNs, also learns different visual representations from...