Duration
14 hours (usually 2 days including breaks)
Requirements
- Experience in the medical industry
- No programming experience is required
Overview
Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep Learning is a subfield of Machine Learning which attempts to mimic the workings of the human brain in making decisions. It is trained with data in order to automatically provide solutions to problems. Deep Learning provides vast opportunities for the medical industry which is sitting on a data goldmine.
In this instructor-led, live training, participants will take part in a series of discussions, exercises and case-study analysis to understand the fundamentals of Deep Learning. The most important Deep Learning tools and techniques will be evaluated and exercises will be carried out to prepare participants for carrying out their own evaluation and implementation of Deep Learning solutions within their organizations.
By the end of this training, participants will be able to:
- Understand the fundamentals of Deep Learning
- Learn Deep Learning techniques and their applications in the industry
- Examine issues in medicine which can be solved by Deep Learning technologies
- Explore Deep Learning case studies in medicine
- Formulate a strategy for adopting the latest technologies in Deep Learning for solving problems in medicine
Audience
- Managers
- Medical professionals in leadership roles
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to Deep Learning
- Impact on the Medical Industry
- Successes and Failures in Deep Learning in Various Industries
Understanding Deep Learning
- Artificial Intelligence and Machine Learning
- Basic Concepts of Deep Learning
- Applications for Deep Learning
- The role of Big Data in Deep Learning
Overview of Common Deep Learning Techniques
- Neural Networks
- Natural Language Processing
- Image Recognition
- Speech Recognition
- Sentiment Analysis
Applying Deep Learning Techniques to Issues in Medicine
- Exploring the Opportunities for Improvement in the Medical Field
- Examining the Applicability of Deep Learning Techniques to the Cited Issues
Exploring Deep Learning Case Studies for Medicine
- DeepVentricle Algorithm for Ventricular Segmentation in Cardiac MR by Arterys
- Skin Cancer Diagnosis Algorithm by Stanford
- Heart Failure Prediction Algorithm by Sutter Health and the Georgia Institute of Technology
- Radiology Scans Diagnoses Across All Modalities by Behold.AI
- Clinical Decision Support Technologies by Enlitic
- Personalized Medicine and Therapies by Deep Genomics
- Decoding Cancer with Freenome
- Detection of Diabetic Retinopathy by Google
- Chatbot for Prevention and Diagnosis of Disease by Babylon Health
Limitations of Deep Learning
Ethical Implications and Data Privacy Concerns in Deep Learning
Creating New Business Models Based on Deep Learning-Enabled Platforms and Ecosystems
Bringing it All Together
- Choosing Deep Learning Solutions that Fit Your Needs
- Strategies for Adoption of Deep Learning Technologies
Team Communication and Managerial Buy-In
- Conversations with Managers and Leaders
- Conversations with Engineers and Data Scientists
Summary and Conclusion