A5001207370- Radiomics and Machine Learning in Medical Imaging
- Artificial Intelligence in Healthcare and Education
- Explainable Artificial Intelligence (XAI)
- Advanced Computing and Algorithms
- Mobile Health and mHealth Applications
- Biosensors and Analytical Detection
Moffitt Cancer Center
2023
Rice University
2020
Cervical cancer disproportionally affects women in low- and middle-income countries, part due to the difficulty of implementing existing cervical screening diagnostic technologies low-resource settings. Single-board computers offer a low-cost alternative provide computational support for automated point-of-care technologies. Here we demonstrate two new devices prevention that use single-board computer: 1) imaging system real-time detection precancer 2) reader interpretation lateral...
Abstract Objective . Developing Machine Learning models (N Gorre et al 2023) for clinical applications from scratch can be a cumbersome task requiring varying levels of expertise. Seasoned developers and researchers may also often face incompatible frameworks data preparation issues. This is further complicated in the context diagnostic radiology oncology applications, given heterogenous nature input specialized requirements. Our goal to provide clinicians, researchers, early AI with...
Building Machine Learning models from scratch for clinical applications can be a challenging undertaking requiring varied levels of expertise. Given the heterogeneous nature input data and specific task requirements, even seasoned developers researchers may occasionally run into issues with incompatible frameworks. This is further complicated in context diagnostic radiology. Therefore, we developed CRP10 AI Application Interface (CRP10AII) as component Medical Imaging Data Resource Center...