A5049531845- AI in cancer detection
- COVID-19 diagnosis using AI
- Anomaly Detection Techniques and Applications
- Data-Driven Disease Surveillance
- Medical Imaging and Analysis
- Radiomics and Machine Learning in Medical Imaging
- Image Retrieval and Classification Techniques
- Advanced Radiotherapy Techniques
- Maritime Navigation and Safety
- Maritime Transport Emissions and Efficiency
- Time Series Analysis and Forecasting
- Multimodal Machine Learning Applications
- Artificial Immune Systems Applications
- Brain Tumor Detection and Classification
- Advanced Image and Video Retrieval Techniques
- Digital Radiography and Breast Imaging
- Simulation Techniques and Applications
- Advanced Neural Network Applications
University of Nevada, Reno
2022-2024
Institute for Advanced Studies in Basic Sciences
2018
Mass segmentation is one of the fundamental tasks used when identifying breast cancer due to comprehensive information it provides, including location, size, and border masses. Despite significant improvement in performance task, certain properties data, such as pixel class imbalance diverse appearance sizes masses, remain challenging. Recently, there has been a surge articles proposing address through formulation loss function. While demonstrating an enhancement performance, they mostly...
Mammography images are the most commonly used tool for breast cancer screening. The presence of pectoral muscle in mediolateral oblique view makes designing a robust automated detection system more challenging. Most current methods removing based on traditional machine learning approaches. This is partly due to lack segmentation masks available datasets. In this paper, we provide INbreast, MIAS, and CBIS-DDSM datasets, which will enable development supervised utilization deep learning....
Detection of out-of-distribution samples is one the critical tasks for real-world applications computer vision. The advancement deep learning has enabled us to analyze data which contain unexplained samples, accentuating need detect instances more than before. GAN-based approaches have been widely used address this problem due their ability perform distribution fitting; however, they are accompanied by training instability and mode collapse. We propose a simple yet efficient...
Detection of out-of-distribution samples is one the critical tasks for real-world applications computer vision. The advancement deep learning has enabled us to analyze data which contain unexplained samples, accentuating need detect instances more than before. GAN-based approaches have been widely used address this problem due their ability perform distribution fitting; however, they are accompanied by training instability and mode collapse. We propose a simple yet efficient...