AI for Medicine Specialization

3 Courses

Intermediate

3 months (7 hours/week)

Pranav Rajpurkar, Amirhossein Kiani, Bora Uyumazturk, Eddy Shyu

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What you will learn

Estimate treatment effects using data from randomized control trials
Explore methods to interpret diagnostic and prognostic models
Apply natural language processing to extract information from unstructured medical data

Skills you will gain

Model interpretation
Image Segmentation
Natural Language Extraction
Machine Learning
Time-To-Event Modeling
Deep Learning
Model Evaluation
Multi-Class Classification
Random Forest
Model Tuning
Treatment Effect Estimation
Machine Learning Interpretation

AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. In this Specialization, you’ll gain practical experience applying machine learning to concrete problems in medicine. You’ll learn how to:

Diagnose diseases from x-rays and 3D MRI brain images
Predict patient survival rates more accurately using tree-based models
Estimate treatment effects on patients using data from randomized trials
Automate the task of labeling medical datasets using natural language processing

3 Courses
3 months (7 hours/week)
Intermediate

Enroll Now

Syllabus

How can AI be applied to medical imaging to diagnose diseases? In this first course, you’ll learn about the nuances of working with both 2D and 3D medical image data, for multi-class classification and image segmentation. You’ll then apply what you’ve learned to classify diseases in x-ray images and segment tumors in 3D MRI brain images. Finally, you’ll learn how to properly evaluate the performance of your models.

Week 1:

Introduction: A conversation with Andrew Ng
Diagnosis examples
Model training on chest x-rays
Training, prediction, and loss
Class imbalance
Binary cross entropy loss function
Resampling methods
Multi-task loss
Transfer learning and data augmentation
Model testing

Week 2:

Introduction: A conversation with Andrew Ng
Evaluation metrics
Accuracy in terms of conditional probability
Sensitivity, specificity, and prevalence
Confusion matrix
ROC curve
Threshold (operating point)
Confidence intervals
Width of confidence intervals and sample size
Using a sample to estimate the population

Week 3:

Introduction: A conversation with Andrew Ng
Representing MRI data
Image registration
2D and 3D segmentation
3D U-Net
Data augmentation for segmentation
Loss function for image segmentation
Soft dice loss
External validation
Retrospective vs. prospective data
Working with cleaned vs. raw data
Measuring patient outcomes
Algorithmic bias
Model influence on medical decision-making

Instructors

Pranav Rajpurkar

Instructor

Computer Science, Stanford University

Amirhossein Kiani

Curriculum Engineer

Product Manager, Google Health

Bora Uyumazturk

Curriculum Developer

Machine Learning Engineer, Viaduct

Eddy Shyu

Senior Curriculum Developer

Product Lead, DeepLearning.AI

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AI for Medicine Specialization

What you will learn

Skills you will gain