To get basic understanding of techniques of machine learning with a focus on deep learning techniques.
To understand supervised and unsupervised learning techniques.
To know current capabilities and limitations of deep learning methodology in the context of radiology.
AI for lesion detection and characterization
To see specific examples of AI application in clinical radiology and research.
To understand challenges, solutions and pitfalls in translating ML methodology to successful clinical implementation.
To learn what AI can add to the management of the radiology department.
Machine learning in medical imaging going forward
To understand machine learning, where current technology development is heading and how it is aligned with clinical needs.
To understand which tasks ML/DL perform well, where the challenges are, where we can expect improvement and what the prerequisites for this are.
To learn what machine learning can add to the management of the radiology department.
Which impact does AI have on medicine?
To be prepared for the future role of AI and radiologists, and their interaction, when using AI as a tool for development and clinical routine.
To understand how AI will impact the role and tasks of radiologists in the future.
To know what the beneficial directions are and how we can facilitate effective joint research at the interface of the involved communities.
To try to foresee what the emerging role of AI means for educating the next generation of radiologists.
Radiomics+: prediction model using convergent data
The discuss the omics revolution and the different techniques used in radiomics and radiomics+.
To present trends in radiomics.
To understand personalised medicine initiatives by the example: where does radiomics+ fit in?
AI and Holomics: predicting the truth from hybrid imaging
To get an overview about shallow and deep learning.
To appreciate supervised, unsupervised and reinforcement learning with examples.
To learn about Holomics: a holistic approach for precision medicine, concept and examples.
Sharing is caring: on the need for open research data
To give an introduction to new concepts of Open Research Databases and Big-Data repository and their impact on development of new paradigm of data-driven patient management and personalised healthcare.
To understand the underlying challenges and requirements as well as the ethical and legal framework that regulates the setup and usage of large collection of patient data for open research.
To review of potential applications in molecular imaging and in support of multi-centric clinical trials.
The impact of AI technologies in patient care: advantages
To know the challenges of knowledge management in radiology.
To know the advantages of machine learning technologies compared to traditional approaches.
To know the technical limitations of machine learning.
How to integrate AI technology in radiology today
To know the challenge: multitude of AI engines to be integrated in one workflow.
To learn how to collect and annotate radiology data for machine learning.
To learn how to organise radiologist's workflow when using AI.
To learn to manage radiology data to be used for machine learning.
How will the introduction of AI change the role of the….
To learn how radiologists will use machine learning in clinical routine.
To learn how machine learning will change the role of the radiologist (doctors-patient relationship, relationship between radiologists and referring physicians).
To learn how radiologists can prepare for machine learning.