step5_models.qmd

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title: "Step 5: Classification Model"
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**Aim: Create a machine learning model to classify slices as low-grade, high-grade, or benign.**

# Summary

The classification model aims to categorize tissue slices as **low-grade, high-grade, or benign C-MIL**. Key considerations include incorporating **patient demographics** (age, gender, lesion location) to enhance accuracy, identifying **melanocyte distribution patterns** for improved classification, and prioritizing **Melan-A and Sox-10** stains over H&E, as they provide clearer visibility of melanocytes. An ensemble approach may be explored to refine predictions.

# Model Considerations

Here are some potential steps/directions to consider when creating classification models. 

## Patient demographics

Using the information from this [file](https://nuwildcat-my.sharepoint.com/:x:/r/personal/akl0407_ads_northwestern_edu/_layouts/15/Doc.aspx?sourcedoc=%7B808B83F5-0AC5-4834-8518-E454850AB281%7D&file=clincal%20data%20sheet%20for%20scoring.xlsx&action=default&mobileredirect=true), add patient information including age, gender, ethnicity, laterality, and location of lesion to our ML model. 

## Pattern recognition

- Ideally, our model needs to be able to identify the distribution and size/shape of melanocytes.
  - Note that `low-grade CMIL's` have no-to-mild atypia, and `high-grade CMIL's`have moderate-to-severe atypia.
- Once we verify a list of discernible patterns, we can create separate models for specific patterns and then use an ensemble model to generate a final classification decision. 

## Melan-A and Sox-10 vs. H&E

Since Melan-A and Sox-10 appear to display melanocyte distribution more clearly than H&E, consider weighing the former two stain types more than the latter when creating our model.