DeepLearning.AI - AI for Medical Diagnosis
4.5 /5- Offered byCoursera
AI for Medical Diagnosis at Coursera Overview
Duration | 19 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
AI for Medical Diagnosis at Coursera Highlights
- Earn a shareable certificate upon completion.
- Flexible deadlines according to your schedule.
AI for Medical Diagnosis at Coursera Course details
- 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. As an AI practitioner, you have the opportunity to join in this transformation of modern medicine. If you're already familiar with some of the math and coding behind AI algorithms, and are eager to develop your skills further to tackle challenges in the healthcare industry, then this specialization is for you. No prior medical expertise is required!
- This program will give you practical experience in applying cutting-edge machine learning techniques to concrete problems in modern medicine:
- - In Course 1, you will create convolutional neural network image classification and segmentation models to make diagnoses of lung and brain disorders.
- - In Course 2, you will build risk models and survival estimators for heart disease using statistical methods and a random forest predictor to determine patient prognosis.
- - In Course 3, you will build a treatment effect predictor, apply model interpretation techniques and use natural language processing to extract information from radiology reports.
- These courses go beyond the foundations of deep learning to give you insight into the nuances of applying AI to medical use cases. As a learner, you will be set up for success in this program if you are already comfortable with some of the math and coding behind AI algorithms. You don't need to be an AI expert, but a working knowledge of deep neural networks, particularly convolutional networks, and proficiency in Python programming at an intermediate level will be essential. If you are relatively new to machine learning or neural networks, we recommend that you first take the Deep Learning Specialization, offered by deeplearning.ai and taught by Andrew Ng.
- The demand for AI practitioners with the skills and knowledge to tackle the biggest issues in modern medicine is growing exponentially. Join us in this specialization and begin your journey toward building the future of healthcare.
AI for Medical Diagnosis at Coursera Curriculum
Disease detection with computer vision
Welcome to the Specialization with Andrew and Pranav
Demo
Recommended prerequisites
Medical Image Diagnosis
Eye Disease and Cancer Diagnosis
Building and Training a Model for Medical Diagnosis
Training, prediction, and loss
Image Classification and Class Imbalance
Binary Cross Entropy Loss Function
Impact of Class Imbalance on Loss Calculation
Resampling to Achieve Balanced Classes
Multi-Task
Multi-task Loss, Dataset size, and CNN Architectures
Working with a Small Training Set
Generating More Samples
Model Testing
Splitting data by patient
Sampling
Ground Truth and Consensus Voting
Additional Medical Testing
Connect with your mentors and fellow learners on Slack
About the automatic grader
How to refresh your workspace
Week 1 Quiz: Disease detection with computer vision
Evaluating models
Sensitivity, Specificity, and Evaluation Metrics
Accuracy in terms of conditional probability
Sensitivity, Specificity and Prevalence
PPV, NPV
Confusion matrix
ROC curve and Threshold
Varying the threshold
Sampling from the Total Population
Confidence intervals
95% Confidence interval
Calculating PPV in terms of sensitivity, specificity and prevalence
Week 2 Quiz: Evaluating machine learning models
Image segmentation on MRI images
Medical Image Segmentation
MRI Data and Image Registration
Segmentation
2D U-Net and 3D U-Net
Data augmentation for segmentation
Loss function for image segmentation
Different Populations and Diagnostic Technology
External validation
Measuring Patient outcomes
Congratulations!
Convolutional Neural networks
More about U-Net (Optional)
Acknowledgements
Citations
Week 3 Quiz: Segmentation on medical images
AI for Medical Diagnosis at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
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