Training AI for histological tasks involves several steps:
Data collection: Gathering a large dataset of histological images, often annotated by experts. Preprocessing: Preparing the data by normalizing images, augmenting the dataset, and removing noise. Model training: Using machine learning algorithms to train models on the preprocessed data, optimizing their performance through validation techniques. Model evaluation: Assessing the model's accuracy, sensitivity, and specificity using test datasets. Deployment: Integrating the trained model into clinical or research workflows for real-world applications.