A5012899268- Medical Image Segmentation Techniques
- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging and Analysis
- Advanced Neural Network Applications
- AI in cancer detection
- Medical Imaging Techniques and Applications
- Brain Tumor Detection and Classification
- Topic Modeling
- Advanced Image and Video Retrieval Techniques
- Advanced Graph Neural Networks
- Advanced MRI Techniques and Applications
- Fetal and Pediatric Neurological Disorders
- Advanced Neuroimaging Techniques and Applications
- COVID-19 diagnosis using AI
- Artificial Intelligence in Healthcare and Education
- Lung Cancer Diagnosis and Treatment
- Data Quality and Management
- Text and Document Classification Technologies
- Traumatic Brain Injury and Neurovascular Disturbances
- Natural Language Processing Techniques
- Advanced Radiotherapy Techniques
- Sentiment Analysis and Opinion Mining
- Prostate Cancer Diagnosis and Treatment
- Cerebrovascular and Carotid Artery Diseases
- Neonatal and fetal brain pathology
United Imaging Healthcare (China)
2018-2024
Bank of China
2022
Harbin Institute of Technology
2022
Beijing University of Posts and Telecommunications
2016-2020
Northwestern Polytechnical University
2016-2019
University of North Carolina at Chapel Hill
2016-2019
Imaging Center
2018
Zhejiang University
2010
The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in 200 countries territories as of April 9, 2020. Detecting COVID-19 at early stage essential to deliver proper healthcare patients also protect uninfected population. To this end, we develop a dual-sampling attention network automatically diagnose from community acquired pneumonia (CAP) chest computed tomography (CT). In particular, propose novel online module with 3D...
In radiotherapy for cancer patients, an indispensable process is to delineate organs-at-risk (OARs) and tumors. However, it the most time-consuming step as manual delineation always required from radiation oncologists. Herein, we propose a lightweight deep learning framework treatment planning (RTP), named RTP-Net, promote automatic, rapid, precise initialization of whole-body OARs Briefly, implements cascade coarse-to-fine segmentation, with adaptive module both small large organs,...
Analysis of modern large-scale, multicenter or diseased data requires deformable registration algorithms that can cope with diverse nature.
Accurate segmentation of pelvic organs (i.e., prostate, bladder, and rectum) from CT image is crucial for effective prostate cancer radiotherapy. However, it a challenging task due to: 1) low soft tissue contrast in images 2) large shape appearance variations organs. In this paper, we employ two-stage deep learning-based method, with novel distinctive curve-guided fully convolutional network (FCN), to solve the aforementioned challenges. Specifically, first stage fast robust organ detection...
Accurate segmentation of the prostate is a key step in external beam radiation therapy treatments. In this paper, we tackle challenging task CT images by two-stage network with 1) first stage to fast localize, and 2) second accurately segment prostate. To precisely stage, formulate into multi-task learning framework, which includes main prostate, an auxiliary delineate boundary. Here, applied provide additional guidance unclear boundary images. Besides, conventional deep networks typically...
Registration of pelvic computed tomography (CT) and magnetic resonance imaging (MRI) is highly desired as it can facilitate effective fusion two modalities for prostate cancer radiation therapy, i.e., using CT dose planning MRI accurate organ delineation. However, due to the large intermodality appearance gaps high shape/appearance variations organs, CT/MRI registration challenging. In this paper, we propose a region-adaptive deformable method multimodal image registration. Specifically,...
Image registration of liver dynamic contrast-enhanced computed tomography (DCE-CT) is crucial for diagnosis and image-guided surgical planning cancer. However, intensity variations due to the flow contrast agents combined with complex spatial motion induced by respiration brings great challenge existing intensity-based methods. To address these problems, we propose a novel structure-aware method incorporating structural information related organs segmentation-guided deep network. Existing...
Multimodal sentiment analysis has attracted increasing attention and lots of models have been proposed. However, the performance state-of-the-art decreases sharply when they are deployed in real world. We find that main reason is real-world applications can only access text outputs by automatic speech recognition (ASR) models, which may be with errors because limitation model capacity. Through further ASR outputs, we some cases words, key elements textual modality, recognized as other makes...
Anatomical labeling of head and neck vessels is a vital step for cerebrovascular disease diagnosis. However, it remains challenging to automatically accurately label in computed tomography angiography (CTA) since are tortuous, branched, often spatially close nearby vasculature. To address these challenges, we propose novel topology-aware graph network (TaG-Net) vessel labeling. It combines the advantages volumetric image segmentation voxel space centerline line space, wherein provides...
Background: The heterogeneity of uterine fibroids in magnetic resonance imaging (MRI) is complex for a subjective visual evaluation, therefore it difficult an accurate prediction the efficacy high intensity focused ultrasound (HIFU) ablation before treatment. purpose this study was to set up radiomics model based on MRI T2-weighted (T2WI) predicting HIFU fibroids, and would be used preoperative screening achieving non-perfused volume ratio (NPVR). Methods: A total 178 patients with were...