AI for Medicine Specialization

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Intermediate

1. Welcome to the AI for Medicine Specialization
- 1.Welcome to the Specialization with Andrew and Pranav.mp4
  
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- 2.Demo.mp4
  
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- 3.Recommended Pre-requisites.mp4
  
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2. Applications of Computer Vision to Medical Diagnosis
- 1.Medical Image Diagnosis.mp4
  
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- 2.Eye Disease and Cancer Diagnosis.mp4
  
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Lab
- C1_W1_Lab_1_data_exploration_and_image_preprocessing.py
  
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3. Handling Class Imbalance and Small Training Sets
- 1.Building and Training a Model for Medical Diagnosis.mp4
  
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- 2.Training, Prediction, and Loss.mp4
  
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- 3.Image Classification and Class Imbalance.mp4
  
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- 4.Binary Cross Entropy Loss Function.mp4
  
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- 5.Impact of Class Imbalance on Loss Calculation.mp4
  
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- 6.Resampling to Achieve Balanced Classes.mp4
  
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- 7.Multi-Task.mp4
  
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- 8.Multi-task Loss, Dataset size, and CNN Architectures.mp4
  
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- 9.Working with a Small Training Set.mp4
  
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- 10.Generating More Samples.mp4
  
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Lab
- C1_W1_Lab_2_counting_labels_and_weighted_loss_function.py
  
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- C1_W1_Lab_3_densenet.py
  
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4. Checking your Model Performance
- 1.Model Testing.mp4
  
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- 2.Splitting Data by Patient.mp4
  
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- 3.Sampling.mp4
  
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- 4.Ground Truth and Consensus Voting.mp4
  
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- 5.Additional Medical Testing.mp4
  
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Lab
- C1_W1_Lab_4_patient_overlap_and_data_leakage.py
  
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Assignment
- C1_W1_Assignment.html
  
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- C1_W1_Assignment.py
  
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- Files.zip
  
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Data
- Files.zip
  
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Quiz
- 1-2.png
  
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- 3-4.png
  
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- 5.png
  
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- 6-7-8-9.png
  
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- 10.png
  
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1. Key Evaluation Metrics
- 1.Sensitivity, Specificity and Evaluation Metrics.mp4
  
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- 2.Accuracy in Terms of Conditional Probability.mp4
  
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- 3.Sensitivity, Specificity and Prevalence.mp4
  
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- 4.PPV, NPV.mp4
  
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- 5.Confusion Matrix.mp4
  
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2. Threshold and Evaluation Metrics
- 1.ROC Curve and Threshold.mp4
  
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- 2.Varying the Threshold.mp4
  
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Lab
- C1_W2_Lab_1_roc_curve _and_threshold.py
  
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- Files.zip
  
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3. Interpreting Confidence Intervals Correctly
- 1.Sampling from the Total Population.mp4
  
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- 2.Confidence Intervals.mp4
  
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- 3.95% Confidence Interval.mp4
  
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Assignment
- C1_W2_Assignment.py
  
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- Files.zip
  
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Quiz
- 1-2.png
  
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- 3-4.png
  
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- 5.png
  
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- 6-7.png
  
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- 8-9.png
  
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1. Exploring MRI Data
- 1.Medical Image Segmentation.mp4
  
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Lab
- C1_W3_Lab_1_explore_mri_data_and_labels.py
  
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2. Image Segmentation
- 1.MRI Data and Image Registration.mp4
  
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- 2.Segmentation.mp4
  
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- 3.2D U-Net and 3D U-Net.mp4
  
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- 4.Data Augmentation for Segmentation.mp4
  
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- 5.Loss Function for Image Segmentation.mp4
  
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Extract a Sub Section
- C1_W3_Lab_2_extract_a_sub_section.py
  
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U-Net Model
- C1_W3_Lab_3_unet_model.py
  
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3. Practical Considerations
- 1.Different Populations and Diagnostic Technology.mp4
  
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- 2.External Validation.mp4
  
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- 3.Measuring Patient Outcomes.mp4
  
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4. Summary of AI for Medical Diagnosis
- 1.Congratulations!.mp4
  
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Assignment
- C1_W3_Assignment.py
  
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- Files.zip
  
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Data
- Files.zip
  
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Quiz
- 1-2-3.png
  
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- 4-5.png
  
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- 6.png
  
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- 7-8-9.png
  
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1. Introduction to Prognostic models
- 1.Course 2 Intro with Andrew and Pranav.mp4
  
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- 2.Prerequisites and Learning Outcomes.mp4
  
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2. What is the Risk of Getting a Disease?
- 1.Medical Prognosis.mp4
  
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Lab
- C2_W1_Lab_1_create_a_linear_model.py
  
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- Files.zip
  
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3. Prognostic Models in Medical Practice
- 1.Examples of Prognostic Tasks.mp4
  
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- 2.Atrial Fibrillation.mp4
  
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- 3.Liver Disease Mortality.mp4
  
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- 4.Risk of Heart Disease.mp4
  
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Lab
- C2_W1_Lab_2_risk_scores_pandas_and_numpy.py
  
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- Files.zip
  
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4. Representing Feature Interactions
- 1.Risk Score Computation.mp4
  
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Lab
- C2_W1_Lab_3_combine_features.py
  
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- Files.zip
  
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5. Evaluating Prognostic Models
- 1.Evaluating Prognostic Models.mp4
  
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- 2.Concordant Pairs, Risk Ties, Permissible Pairs.mp4
  
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- 3.C-Index.mp4
  
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Lab
- C2_W1_Lab_4_concordance_index.py
  
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- Files.zip
  
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Assignment
- C2_W1_Assignment.ipynb
  
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- Files.zip
  
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Quiz
- 1-2.png
  
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- 3-4.png
  
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- 5-6.png
  
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- 7-8.png
  
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- 9-10.png
  
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1. Tree-base Models
- 1.Decision Trees for Prognosis.mp4
  
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- 2.Decision Trees.mp4
  
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- 3.Dividing the Input Space.mp4
  
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- 4.Building a Decision Tree.mp4
  
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- 5.How to Fix Overfitting.mp4
  
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Lab
- C2_W2_Lab_1_decision_tree_classifier.py
  
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- Files.zip
  
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2. Identifying Missing Data
- 1.Survival Data.mp4
  
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- 2.Different Distributions.mp4
  
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- 3.Missing Data Example.mp4
  
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- 4.Missing Completely at Random.mp4
  
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- 5.Missing at Random.mp4
  
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- 6.Missing Not at Random.mp4
  
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Lab
- C2_W2_Lab_2_missing_data_and_applying_a_mask.py
  
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- Files.zip
  
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3. Using Imputation to Handle Missing Data
- 1.Imputation.mp4
  
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- 2.Mean Imputation.mp4
  
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- 3.Regression Imputation.mp4
  
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- 4.Calculate Imputed Values.mp4
  
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Lab
- C2_W2_Lab_3_imputation.py
  
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- Files.zip
  
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Assignment
- C2_W2_Assignment.py
  
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- Files.zip
  
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Quiz
- 1.png
  
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- 2-3-4.png
  
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- 5-6-7.png
  
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- 8-9.png
  
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1. Survival Estimates
- 1.Survival Models.mp4
  
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- 2.Survival Function.mp4
  
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- 3.Valid Survival Functions.mp4
  
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2. Time to Event Data
- 1.Collecting Time Data.mp4
  
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- 2.When a Stroke is Not Observed.mp4
  
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- 3.Heart Attack Data.mp4
  
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- 4.Right Censoring.mp4
  
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3. Estimate Survival with Censored Data
- 1.Estimating the Survival Function.mp4
  
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- 2.Died Immediately, or Never Die.mp4
  
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- 3.Somewhere in-between.mp4
  
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- 4.Using Censored Data.mp4
  
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- 5.Chain Rule of Conditional Probability.mp4
  
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- 6.Deriving Survival.mp4
  
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- 7.Calculating Probabilities from the Data.mp4
  
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- 8.Comparing Estimates.mp4
  
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- 9.Kaplan Meier Estimate.mp4
  
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Lab
- C2_W3_Lab_1_counting_patients.py
  
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- C2_W3_Lab_2_kaplan_meier.py
  
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Counting Patients
- Files.zip
  
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Kaplan Meier
- Files.zip
  
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Assignment
- C2_W3_Assignment.py
  
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- Files.zip
  
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Quiz
- 1-2-3-4.png
  
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- 5.png
  
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- 6.png
  
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1. Survival and Hazard Functions
- 1.Hazard Functions.mp4
  
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- 2.Hazard.mp4
  
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- 3.Survival to Hazard.mp4
  
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- 4.Cumulative Hazard.mp4
  
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Lab
- C2_W4_Lab_1_categorical_variables.py
  
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- Files.zip
  
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2. Customizing Risk Models to Individual Patients
- 1.Individualized Predictions.mp4
  
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- 2.Relative Risk.mp4
  
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- 3.Ranking Patients by Risk.mp4
  
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- 4.Individual vs Baseline Hazard.mp4
  
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- 5.Smoker vs Non-smoker.mp4
  
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- 6.Effect of Age on Hazard.mp4
  
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- 7.Risk Factor Increase Per Unit Increase in a Variable.mp4
  
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- 8.Risk Factor Increase or Decrease.mp4
  
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Lab
- C2_W4_Lab_2_hazard_function.py
  
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- Files.zip
  
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3. Non-linear Risk Models with Survival Trees
- 1.Intro to Survival Trees.mp4
  
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- 2.Survival Tree.mp4
  
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- 3.Nelson Aalen Estimator.mp4
  
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- 4.Comparing Risks of Patients.mp4
  
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- 5.Mortality Score.mp4
  
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4. Evaluate Survival Models
- 1.Evaluation of Survival Model.mp4
  
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- 2.Permissible and Non-Permissible Pairs.mp4
  
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- 3.Possible Permissible Pairs.mp4
  
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- 4.Example of Harrells C-Index.mp4
  
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- 5.Example of Concordant Pairs.mp4
  
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- 6.Week 4 Summary.mp4
  
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Lab
- C2_W4_Lab_3_permissible_pairs_with_censoring_and_time.py
  
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- Files.zip
  
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- 5. Congratulations!.mp4
  
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Assignment
- C2_W4_Assignment.py
  
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- Files.zip
  
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Quiz
- 1-2.png
  
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- 3-4.png
  
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- 5-6.png
  
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- 7.png
  
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- 8.png
  
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- 9.png
  
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1. Introduction
- 1.Intro to Course 3 with Andrew and Pranav.mp4
  
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- 2.About Course 3.mp4
  
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2. Randomized Control Trials
- 1.Absolute Risk Reduction.mp4
  
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- 2.Randomized Control Trials.mp4
  
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Lab
- C3_W1_Lab_1_pandas_for_a_medical_dataset.py
  
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- Files.zip
  
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3. Average Treatment Effect
- 1.Causal Inference.mp4
  
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- 2.Average Treatment Effect.mp4
  
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- 3.Conditional Average Treatment Effect.mp4
  
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- 4.T-Learner.mp4
  
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- 5.S-Learner.mp4
  
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Lab
- C3_W1_Lab_2_model_training_basics_with_sklearn.py
  
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- Files.zip
  
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4. Evaluate Individualized Treatment Effect
- 1.Causal Inference.mp4
  
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- 2.Average Treatment Effect.mp4
  
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- 3.Conditional Average Treatment Effect.mp4
  
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- 4.T-Learner.mp4
  
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- 5.S-Learner.mp4
  
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Lab
- C3_W1_Lab_3_logistic_regression_model_interpretation.py
  
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- Files.zip
  
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Assignment
- C3_W1_Assignment.py
  
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- Files.zip
  
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Quiz
- 1.png
  
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- 2-3.png
  
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- 4.png
  
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- 5.png
  
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- 6-7-8.png
  
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1. Question Answering
- 1.Medical Question Answering.mp4
  
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- 2.Handling Words with Multiple Meanings.mp4
  
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- 3.Define the Answer in a Text.mp4
  
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Lab
- C3_W2_Lab_1_cleaning_text.ipynb
  
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- C3_W2_Lab_1_cleaning_text.py
  
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- Files.zip
  
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2. Automatic Labeling
- 1.Automatic Label Extraction for Medical Imaging.mp4
  
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- 2.Synonyms for Labels.mp4
  
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- 3.Is-a Relationships for Labels.mp4
  
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- 4.Presence or Absence of a Disease.mp4
  
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Lab
- C3_W2_Lab_2_bioc_and_negbio.ipynb
  
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- C3_W2_Lab_2_bioc_and_negbio.py
  
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- Files.zip
  
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3. Evaluate Automatic Labeling
- 1.Evaluating Label Extraction.mp4
  
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- 2.Precision, Recall and F1 Score.mp4
  
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- 3.Evaluating on Multiple Disease Categories.mp4
  
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Lab
- C3_W2_Lab_3_prep_input_for_text_classification.ipynb
  
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- C3_W2_Lab_3_prep_input_for_text_classification.py
  
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- Files.zip
  
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Assignment
- C3_W2_Assignment.ipynb
  
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- C3_W2_Assignment.py
  
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- Files.zip
  
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Quiz
- 1-2.png
  
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- 3.png
  
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- 4.png
  
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- 5-6.png
  
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- 7-8.png
  
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- 9.png
  
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- 10.png
  
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1. Feature Importance
- 1.Drop Column Method.mp4
  
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- 2.Permutation Method.mp4
  
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Lab
- C3_W3_Lab_1_permutation_method.py
  
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2. Individual Feature Importance
- 1.Individual Feature Importance.mp4
  
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- 2.Shapley Values.mp4
  
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- 3.Combining Importances.mp4
  
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- 4.Shapley Values for all Patients.mp4
  
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3. Interpreting Deep Learning Models
- 1.Interpreting CNN Models.mp4
  
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- 2.Localization Maps.mp4
  
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- 3.Heat Maps.mp4
  
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Lab
- C3_W3_Lab_2_intro_to_gradcam.py
  
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- C3_W3_Lab_3_gradcam_continuation.py
  
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Assignment
- C3_W3_Assignment.py
  
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- Files.zip
  
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Data
- Files.zip
  
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Quiz
- 1.png
  
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- 2-3.png
  
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- 4.png
  
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- 5.png
  
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- 6.png
  
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