Posted by Laurence Moroney, AI Advocate

I’m excited to share an important aspect of the TensorFlow community: when educators and domain experts teach and train developers how to use machine learning technology to solve important tasks for a variety of scenarios, including in health care. To this end, deeplearning.ai and Coursera have launched an “AI for Medicine” specialization using TensorFlow.
Nothing excites our team more than when we see how others are using TensorFlow to solve real-world problems. In this three course specialization introduced by Andrew Ng and taught by Pranav Rajpurkar, we hope to widen access so that more people can understand the needs of medical Machine Learning problems.

Deeplearning.ai and Coursera have designed a specialization that is divided into three courses. The first Machine Learning for Medical Diagnosis will take you through some hypothetical Machine Learning scenarios for diagnosis of medical issues. In the first week, you’ll explore scenarios like detecting skin cancer, eye disease and histopathology. You’ll get hands-on with how you can write code in TensorFlow using convolutional neural networks to examine images, which, for example can be used to identify different conditions in an X-Ray.

The course does require some knowledge of TensorFlow, using techniques such as convolutional neural networks, transfer learning, natural language processing, and more. I recommend that you take the TensorFlow: In Practice specialization to understand the coding skills behind it, and the Deep Learning Specialization to go deeper into how the underlying technology works. Another great resource to learn the techniques used in this course is the book “Hands on Machine Learning with SciKit-Learn, Keras and TensorFlow” by Aurelien Geron.

One of the things I really enjoyed about the course is the balance of medical terminology and using common machine learning techniques from TensorFlow, such as data augmentation, to improve your models. Note: all of the data used in the course is de-identified.

Exercises from Rajpurkar’s and Ng’s course: Using image augmentation to extend the effective size of a dataset.

The course continues with techniques such as evaluation metrics and isolating key ones and understanding how to interpret confidence intervals accurately.

The first course wraps up with another deep dive into image processing, this time using segmentation in MRI images, wrapping up with a programming assignment in doing brain tumor auto segmentation on MRIs.

The second course in the specialization will be on Machine Learning for Medical Prognosis where you learn to build models to predict future patient health. You’ll learn techniques to extract data from reports such as a patient’s health metrics, history, and demographics to predict their risk of a major event such as a heart attack.

The third, and final, course will be on Machine Learning for Medical Treatment, where models may be used to assist in medical care to predict what the potential effect of a medical treatment might be on a patient. It will also go into using machine learning for text so that you can use NLP techniques to extract information from radiography reports to get labels or get the basis for a bot for answering medical questions.

In the words of Andrew Ng, “Even if your current work is not in medicine, I think you will find the application scenarios and the practice of these scenarios to be really useful, and maybe this specialization will inspire you to get more interested in medicine”.

The specialization is available at Coursera, and like all courses can be audited for free. You can learn more about deeplearning.ai at their website, and about TensorFlow at tensorflow.org.