Outline

Grasp CNN architecture, including convolution, pooling, and activation layers. • Address pathology-specific challenges like stain variation and magnification levels. • Implement data preparation techniques such as WSI patching and color normalization.

Outline

Learn image preprocessing, annotation, and feature extraction techniques specific to pathology. • Apply data augmentation strategies to enhance model robustness. • Prepare digital pathology images for AI model input.

Outline

Choose appropriate CNN architectures like ResNet, VGG, DenseNet, or EfficientNet. • Implement dataset splitting and validation methods. • Handle class imbalance and select effective evaluation metrics. • Train a CNN model for tissue classification in a hands-on session.

Outline

Perform hyperparameter tuning and apply regularization methods. • Utilize early stopping and learning rate scheduling for efficient training. • Implement transfer learning with pre-trained models and fine-tuning for pathology. • Interpret model decisions using techniques like Grad-CAM.

Outline

Evaluate and validate AI models for clinical relevance and accuracy. • Apply developed AI models to real-world pathology datasets and case studies. • Gain insights into improving diagnostic accuracy and personalized patient care.