ONLINE Practical Deep Learning for Radiologists Fellowship

21 - 23 Jun 2021

100% Online

Level II - General radiologist

9 EACCME CME Credits

Would you like to learn the basics of Machine Learning to incorporate it into your research daily clinical practice and research projects? In this online fellowship you will acquire knowledge about how a convolutional neural network works, being able to experiment in real time with the main parameters that influence its training.

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Description

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.

The first session will be focused on data acquisition and manipulation in order to optimize for a Deep Learning task, what the main hindrances are and how to solve them. We will also cover the basic requirements and tools needed for DL in the radiology setting.

The second session will cover a hands-on neural network training pipeline in which we will tweak the parameters of a neural network to understand how they influence the final result. We will also provide tips and advice on how to start the DL journey in your own department.

The last session will be dedicated to model evaluation, how the main metrics are useful in different case scenarios, and their limitations in a “real-life” clinical setting. We will also 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.

We will also cover the main limitations of Deep Learning in the field of medical imaging and discuss basic notions and resources to start experimenting with Deep Learning algorithms in the daily clinical practice. Each day, a series of selected topics will be presented, followed by a guided interactive activity and group discussion. At the end of every session, we will take some time to explain and debate Deep Learning implementation scenarios in the field of Radiology.


Availability

Maximum number of seats: 10

Learning objectives

  • Learn in a practical way how a neural network works, its strengths and limitations.
  • Familiarize yourself with key concepts of DL evaluation to be able to critically appraise the scientific literature.
  • Discover resources and tools to start experimenting with DL models in your daily practice.

Programme will include

  • Lectures on key points of the Deep Learning pipeline: data preparation, model training and model evaluation.
  • Individual guided “hands-on” sessions with a real convolutional neural network, with group discussion of the results.
  • Lectures on critical reading, as well as on opportunities and obstacles of the practical implementation of Deep Learning tools in the daily workflow of a Radiology Department.

Level

Level II - General radiologist


Technical requirements

  • A computer with Google Chrome and Internet Access

Featured video

Lecturers

  • Daniel Eiroa M.D.
    Spain

    Daniel Eiroa is a Radiology Consultant and Data Analyst currently working at Vall d'Hebron Barcelona Hospital Campus. FER-AIRP 2017 Fellowship Recipient. Dr. Eiroa has authored more than 50 communications, scientific articles and book chapters. He regularly participates in training sessions and lectures on AI and Medical Data Science.

Schedule

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Monday - June 21st 2021
13:30 - 13:40 (GMT +0200) Introduction to the course Introduction
13:40 - 14:05 (GMT +0200) Data gathering, wrangling and manipulation I Lecture Daniel Eiroa
14:05 - 14:35 (GMT +0200) Practical exercise Case demo
14:35 - 14:45 (GMT +0200) Break Break
14:45 - 15:10 (GMT +0200) Data gathering, wrangling and manipulation II Lecture Daniel Eiroa
15:10 - 15:40 (GMT +0200) Practical exercise Case demo
15:40 - 16:10 (GMT +0200) Discussion
16:10 - 16:20 (GMT +0200) Break Break
16:20 - 17:00 (GMT +0200) DL in my Department: What do I need? Lecture Daniel Eiroa
17:00 - 17:30 (GMT +0200) Q&A
Tuesday - June 22nd 2021
13:30 - 14:15 (GMT +0200) CNNs: subsetting, k-fold CV, hyperparameters and training Lecture Daniel Eiroa
14:15 - 14:45 (GMT +0200) Practical exercise Case demo
14:45 - 14:55 (GMT +0200) Break Break
14:55 - 15:40 (GMT +0200) CNNs: hyperparameters and training Lecture Daniel Eiroa
15:40 - 16:10 (GMT +0200) Practical exercise Case demo
16:10 - 16:30 (GMT +0200) Discussion
16:30 - 16:40 (GMT +0200) Break Break
16:40 - 17:00 (GMT +0200) DL in my Department: How do I do? Lecture
17:00 - 17:30 (GMT +0200) Q&A
Wednesday - June 23rd 2021
13:30 - 14:00 (GMT +0200) Model evaluation I Lecture Daniel Eiroa
14:00 - 14:30 (GMT +0200) Practical exercise Case demo
14:30 - 14:50 (GMT +0200) Discussion
14:50 - 15:00 (GMT +0200) Break Break Daniel Eiroa
15:00 - 15:30 (GMT +0200) Critical assessment of Medical ML research Lecture
15:30 - 16:10 (GMT +0200) Practical exercise Case demo
16:10 - 16:40 (GMT +0200) Discussion Daniel Eiroa
16:40 - 16:50 (GMT +0200) Break Case demo
16:50 - 17:30 (GMT +0200) Q&A and closing remarks

Cancellation Policy

What is the cancellation/refund policy?

TMC Academy offers a 7-day money back guarantee from the moment of the online fellowship purchase. 

How do I request a refund?

Send us an email at academy.info@telemedicineclinic.com and we will process your refund.