Transversal Challenges
Artificial Intelligence
Radiomics and Artificial Intelligence in Diagnostic (RAID)
24-T10
October 7th 2024 - November 4th 2024
COURSE COORDINATORS
André Cacito (RT) ,
Luis Freire (Bio Phys, PhD) ,
Maria Margarida Ribeiro (Técnica de Radiologia, Doutora)
COURSE PRESENTATION
The integration of radiomics and artificial intelligence (AI) into current clinical practice offers immense potential in the development of new diagnostic and treatment approaches.
This course is designed to equip participants with an in-depth understanding of the principles of radiomics, advanced imaging technologies and the implementation of AI-based tools.
By combining theoretical knowledge with practical applications, this course aims to equip clinical professionals with the knowledge and skills to, from the huge variety and quantity of radiomics data, be able to use the best AI techniques for accurate detection of diseases and therapy assessment.
This course is designed to equip participants with an in-depth understanding of the principles of radiomics, advanced imaging technologies and the implementation of AI-based tools.
By combining theoretical knowledge with practical applications, this course aims to equip clinical professionals with the knowledge and skills to, from the huge variety and quantity of radiomics data, be able to use the best AI techniques for accurate detection of diseases and therapy assessment.
TARGET AUDIENCE
Graduates in the Medical field, Medical Physics, Health Sciences, Nursing, Bioengineering, Biotechnology, Biology, Bioinformatics or Life Sciences
LEARNING OBJECTIVES >> KNOWLEDGE AND SKILLS TO DEVELOP
- Equip students with a comprehensive understanding of the mathematical and statistical principles subjacent to Radiomics and Artificial Intelligence (R&AI)
- Enable students to understand the handling of relevant data to radiomics, ensuring they are well-prepared to work with complex medical datasets.
- Introduce students to fundamental programming concepts, enabling them to implement and develop algorithms for radiomics and AI applications.
- Develop a comprehensive understanding of machine learning and deep learning techniques, empowering students to leverage these advanced methodologies in radiomics and AI research.
- Encourage students to apply their theoretical knowledge practically, with a specific focus on real-world applications such as Medical Diagnosis, Segmentation, Oncologic Assessment, and Precision Medicine.
- Instruct students on integrating radiomics techniques into the creation of nomograms, enhancing the potential for personalized medical interventions and treatment plans.
- Promote the exchange of experiences, doubts, ideas on all the topics covered in this course.
- Guide students in developing tailored solutions for the implementation of radiomics methodologies in diagnostic procedures (in the final work).
ADMISSION CRITERIA
CV
