Research
IvyGPT: InteractiVe Chinese pathwaY language model in medical domain
General large language models (LLMs) such as ChatGPT have shown remarkable success.
Image Quality Improvementn
Mobile ultrasound devices play a vital role in emergency and family doctor services. However, the image quality of these devices, compared to traditional medical ultrasound equipment, leaves room for improvement.
Chest CT-IQA: A Multi-Task Model for Chest CT Image Quality Assessment
In recent years, especially during the COVID-19 pandemic,a large number of Computerized Tomography (CT) imagesare produced every day for the purpose of inspecting lung diseases.
Advanced U-Net for Multi-class Segmentation of Mammography
Detecting cancer in its early stages through accurate, efficient, and economical methods is essential to reduce its impact.
Self-Correcting Face Detection for Neonatal Heart Rate Estimation
Remote photoplethysmography (rPPG) is a non-invasive method that monitors heart rate (HR) and other vital signs by detecting tiny color changes in the face caused by blood flow under the skin.
Automatic segmentation and direct calculation of radiotherapy
The quality of multi-focus microscopic image fusion hinges upon the precision of the image registration technology.
Nuclear Medicine Imaging Analysis
Nuclear medicine imaging uses radioactive tracers to reveal cellular physiology, providing key insights for diagnosis and treatment. It transcends traditional imaging by focusing on molecular activity, promoting personalized medicine.
Deep learning framework for microscope multi-focus images
A hybrid supervised fusion deep learning framework for microscope multi-focus images
Breast cancer detection and diagnosis
The application of the computer-aided technology in medical imaging has achieved great success. We aim to research the computer-aided breast cancer detection and diagnosis on various kinds of medical images
Multi-modality
Our research focuses on the intricate world of cross-organ, multi-modality imaging, delving into a diverse array of medical imaging techniques
Application of AI algorithms in diagnosis of fundus diseases
The application of artificial intelligence algorithms in the diagnosis of retinal diseases is progressively leading the transformation of the field of medicine.
Da Vinci surgical robot
Computer-assisted surgery (CAS) has ushered in a transformative era for minimally invasive procedures, with the DaVinci Surgical System at the forefront as a cutting-edge robotic surgical platform
MRI Imaging Analysis
Analysis of MRI images included APTw imaging analysis and tumor segmentation, whole-genome analysis, and denoising.
Video-based animal behaviour recognition
With the increasing demand of animal products has brought animal welfare analysis, as more researcher involves, especially into the analysis of the social interactions inside commercial farms, For instance, disruptive inter-animal interaction, e.g. tail-biting, stepping others, etc.
PCB Circuit Board Defect Detection
Efficient PCB defect detection is vital for maintaining high-quality and reliable electronic devices, leading to benefits such as improved product quality, cost reduction, regulatory compliance, brand reputation, and enhanced device lifespan. Additionally, it enhances market competitiveness by meeting industry standards.
MR Breast Artificial Intelligence Research
The study of breast dense tissue is of great significance for the evaluation of breast health, the early detection of breast diseases and the risk assessment of breast cancer
Universal Ultrasound Model
Ultrasound imaging is widely utilized in clinical settings due to its cost-effectiveness, mobility, and safety. While current research in medical universal AI predominantly focuses on language models and general image segmentation, our study introduces a novel universal framework tailored specifically for ultrasound applications
Anatomy-aware Unified Model for MR-imaging-only Radiotherapy
In radiation therapy planning, CT images provide crucial tissue electron density information for dose calculation. However, CT scans expose patients to additional radiation. Although MRI images do not produce radiation, their unique signal characteristics require precise mapping to corresponding CT manifestations to ensure the effectiveness and safety of treatment. Therefore, generating high-quality pseudo-CT images from specific organ MRI data is of significant importance.
Early Prediction of Disease Onset Based on Longitudinal Electronic Health Records
Breast cancer stands as a formidable challenge to women's health, compounded by the current absence of an effective treatment. This underscores the pivotal role of early detection and diagnosis in mitigating the risk of mortality. Over the past few decades, an array of enhancement techniques, including X-rays (mammography), ultrasound, and magnetic resonance imaging (MRI), has been deployed to offer intricate insights into mammogram images, streamlining the detection of breast cancer. While these methods excel in screening for breast tumors, they fall short in monitoring patients' diverse stages and grapple with the challenges of predicting diseases in advance.
Automatic Detection of Breast Lesions in Automated 3D Breast Ultrasound with Cross-Organ Transfer Learning
This research highlights the potential of transfer learning and contrastive learning in enhancing CAD performance for breast cancer detection
Continual Learning for Breast Lesion Segmentation on Mammograms via Mamba and CLIP
Despite deep learning (DL) showing promising potential in medical image segmentation (e.g., breast lesion segmentation), continuous learning is necessary to adapt to improve its generalization for new tasks or new datasets. In clinical practice, however, due to the inaccessibility of previously used sensitive patient data, current DL methods often tend to forget previously learned tasks when trained on new tasks, which is known as Catastrophic Forgetting (CF).
AI-Enhanced Mammogram Analysis: Precision in Breast Calcification and Mass Segmentation
Breast cancer claims many lives annually. Early and accurate detection via AI-enhanced mammography improves diagnosis of calcifications and masses, easing radiologists' workloads and boosting patient survival rates. Our project uses a modified U-Net model
A Multi-modal Approach for Enhanced Breast Cancer Diagnosis and Pathological Subtype Classification
We introduce 3MT-Net, a multi-modal.This approach enhances the accuracy of diagnosing benign and malignant breast tumors and classifying their pathological subtypes
Breast Ultrasound Detection Based on Self-supervised Learning using Large-scale Datasets
Advancements in deep learning and computer vision provide promising solutions for medical image analysis, potentially improving healthcare and patient outcomes.Self-supervised learning has the potential to make significant contributions to the development of robust medical imaging models through its ability to learn useful insights from copious medical datasets without labels.
