ONLINE Practical Deep Learning for Radiologists Fellowship

The upsurge of Machine Learning and Deep Learning algorithms paired with the high amount of digital data generated in radiology is changing this medical specialty. Deep Learning algorithms, in particular convolutional networks, are promising techniques for processing medical imaging data in a wide variety of clinical scenarios. During the first session, a brief introduction to the typical concepts of artificial intelligence will be given. We will then focus on data acquisition and manipulation in order to optimize for a Deep Learning task, what the main hindrances are and how to solve them, followed by an introduction to the basic bits and pieces needed in order to initialize a Neural Network. The second session will cover a hands-on neural network training pipeline in which we will tweak the different pulls and levers and play with the network in order to understand how this influences the final results. We will end the session with two less technical lectures that will focus on the basic requirements and tools needed for DL in the radiology setting as well as tips and advice on how to start the DL journey in your own department.

The last session will be dedicated to model evaluation, how are the main metrics useful in different case scenarios, and their limitations in a “real-life” clinical setting. We will also discuss the ethical and professional implications of AI in the daily clinical practices as well as review and work on the guidelines for critical assessment of AI research.

Throughout the course we will also see other applications of DL in the healthcare setting, such as study protocolization, structured reporting and communication of incidental findings, among others.

Topics: Artificial Intelligence, Online Training, Radiology, CME