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Course: AI for Medical Diagnosis. Click here to go back.
- 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
- 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
- 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!