A5104128667- Radiomics and Machine Learning in Medical Imaging
- AI in cancer detection
- COVID-19 diagnosis using AI
- Pulmonary Hypertension Research and Treatments
- Cutaneous Melanoma Detection and Management
- Lung Cancer Diagnosis and Treatment
- Medical Imaging Techniques and Applications
- Genital Health and Disease
- Medical Imaging and Analysis
- Advanced Image Processing Techniques
- Genetics, Aging, and Longevity in Model Organisms
- Cell Image Analysis Techniques
- Venous Thromboembolism Diagnosis and Management
- Ultrasound in Clinical Applications
- Long-Term Effects of COVID-19
- Medical Image Segmentation Techniques
- Hemodynamic Monitoring and Therapy
- Privacy-Preserving Technologies in Data
- Brain Tumor Detection and Classification
King Abdullah University of Science and Technology
2022-2025
University of Science and Technology of China
2020
Abstract Large language models (LLMs) are seen to have tremendous potential in advancing medical diagnosis recently, particularly dermatological diagnosis, which is a very important task as skin and subcutaneous diseases rank high among the leading contributors global burden of nonfatal diseases. Here we present SkinGPT-4, an interactive dermatology diagnostic system based on multimodal large models. We aligned pre-trained vision transformer with LLM named Llama-2-13b-chat by collecting...
Abstract Tremendous efforts have been made to improve diagnosis and treatment of COVID-19, but knowledge on long-term complications is limited. In particular, a large portion survivors has respiratory complications, currently, experienced radiologists state-of-the-art artificial intelligence systems are not able detect many abnormalities from follow-up computerized tomography (CT) scans COVID-19 survivors. Here we propose Deep-LungParenchyma-Enhancing (DLPE), computer-aided detection (CAD)...
Abstract Large language models (LLMs) are seen to have tremendous potential in advancing medical diagnosis recently. However, it is important note that most current LLMs limited text interaction alone. Meanwhile, the development of multimodal large for still its early stages, particularly considering prevalence image-based data field diagnosis, among which dermatological a very task as skin and subcutaneous diseases rank high leading contributors global burden nonfatal diseases. Inspired by...
Foundation models have attracted significant attention for their impressive generalizability across diverse downstream tasks. However, they are demonstrated to exhibit great limitations in representing high-frequency components and fine-grained details. In many medical imaging tasks, precise representation of such information is crucial due the inherently intricate anatomical structures, sub-visual features, complex boundaries involved. Consequently, limited prevalent foundation can result...
Global population aging presents increasing challenges to healthcare systems, with coronary artery disease (CAD) responsible for approximately 17.8 million deaths annually, making it a leading cause of global mortality. As CAD is largely preventable, early detection and proactive management are essential. In this work, we introduce DigitalShadow, an advanced warning system CAD, powered by fine-tuned facial foundation model. The pre-trained on 21 images subsequently into LiveCAD, specialized...
Pulmonary artery-vein segmentation is critical for disease diagnosis and surgical planning. Traditional methods rely on Computed Tomography Angiography (CTPA), which requires contrast agents with potential health risks. Non-contrast CT, a safer more widely available approach, however, has long been considered impossible this task. Here we propose High-abundant Artery-vein Segmentation (HiPaS), enabling accurate across both non-contrast CT CTPA at multiple resolutions. HiPaS integrates...
Abstract Heterogeneous data is endemic due to the use of diverse models and settings devices by hospitals in field medical imaging. However, there are few open-source frameworks for federated heterogeneous image analysis with personalization privacy protection simultaneously without demand modify existing model structures or share any private data. In this paper, we proposed PPPML-HMI, an learning paradigm personalized privacy-preserving analysis. To our best knowledge, were achieved first...
Pulmonary artery-vein segmentation is crucial for diagnosing pulmonary diseases and surgical planning, traditionally achieved by Computed Tomography Angiography (CTPA). However, concerns regarding adverse health effects from contrast agents used in CTPA have constrained its clinical utility. In contrast, identifying arteries veins using non-contrast CT, a conventional low-cost examination routine, has long been considered impossible. Here we propose High-abundant Artery-vein Segmentation...
Heterogeneous data is endemic due to the use of diverse models and settings devices by hospitals in field medical imaging. However, there are few open-source frameworks for federated heterogeneous image analysis with personalization privacy protection simultaneously without demand modify existing model structures or share any private data. In this paper, we proposed PPPML-HMI, an learning paradigm personalized privacy-preserving analysis. To our best knowledge, were achieved first time under...
Elegans are one of the best model organisms in neural researches, and tropism movement is a typical learning memorizing activity. Based on imaging technique called Fast Track-Capturing Microscope (FTCM), we investigated regulation. Two patterns extracted from various trajectories through analysis turning angle. Then applied this classification trajectory regulation compound gradient field, theoretical results corresponded with experiments well, which can initially verify conclusion. Our...