Predictive modeling in histology typically involves several steps:
Data Collection: Gathering histological data from tissue samples, including images and quantitative measurements. Data Preprocessing: Cleaning and normalizing the data to ensure it is suitable for analysis. Feature Extraction: Identifying relevant features or biomarkers from the histological data. Model Training: Using machine learning algorithms to train models on historical data. Model Validation: Testing the model on a separate dataset to evaluate its accuracy and reliability. Prediction: Applying the model to new data to make predictions about clinical outcomes.
