A5101653641- Radiomics and Machine Learning in Medical Imaging
- Medical Image Segmentation Techniques
- Image Processing Techniques and Applications
- Brain Tumor Detection and Classification
- Medical Imaging and Analysis
- Advanced Biosensing Techniques and Applications
- Traffic control and management
- AI in cancer detection
- Impact of Light on Environment and Health
- COVID-19 diagnosis using AI
- Artificial Intelligence in Healthcare and Education
- Smart Parking Systems Research
Nvidia (United States)
2022-2024
Vanderbilt University
2024
Yangzhou University
2021
The lack of annotated datasets is a major bottleneck for training new task-specific supervised machine learning models, considering that manual annotation extremely expensive and time-consuming. To address this problem, we present MONAI Label, free open-source framework facilitates the development applications based on artificial intelligence (AI) models aim at reducing time required to annotate radiology datasets. Through researchers can develop AI focusing their domain expertise. It allows...
Precise identification of multiple cell classes in high-resolution Giga-pixel whole slide imaging (WSI) is critical for various clinical scenarios. Building an AI model this purpose typically requires pixel-level annotations, which are often unscalable and must be done by skilled domain experts (e.g., pathologists). However, these annotations can prone to errors, especially when distinguishing between intricate types podocytes mesangial cells) using only visual inspection. Interestingly, a...
The entrance and exit area of parking facilities has the characteristics high concentration urban traffic prominent intertwining phenomenon, which easily induces rapid congestion mixed heterogeneous at specific times local locations quickly spreads to entire road section or even a larger area. In order better understand distribution propagation effects access from mid microperspective, 5 m <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>∗</a:mi> </a:math> lane width pixel...