Step 5: Classification Model

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, 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'shave 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.