1. Text Preprocessing: This involves cleaning and preparing the text data for analysis. Steps include tokenization, removing stop words, and stemming or lemmatization. 2. Named Entity Recognition (NER): NER identifies and classifies key entities in the text, such as cell types, tissues, and diseases. 3. Text Classification: This technique categorizes text into predefined classes. For example, a histological report could be classified into benign or malignant categories. 4. Sentiment Analysis: Although less common in histology, sentiment analysis can be used to gauge the tone and urgency of clinical reports. 5. Machine Translation: Translating histological texts from one language to another while preserving the original meaning.
