index.qmd

---
title: "STAT390 CMIL Classification" 
---

**Aim: The objective of this project is to identify the severity of potential eye cancer by looking at a particular eye tissue of the patient.**

In medical terms, we want to develop a machine learning model to accurately classify *Conjunctival melanocytic intraepithelial lesions **(C-MIL)*** as per the *WHO 2022 classification system*. Providing a reproducible and accurate grading of C-MIL will help doctors select the most appropriate management plan for the patient.

The Northwestern University STAT390 Class has made the following progress on this project:

![](images/workflow_diagram2.png){fig-align="center"}

**Step 1:**  Extracting tissue slices from Whole Slice Images (**WSI**) using QuPath

**Step 2:**  Matching similar tissue slices across the different stains (**H&E, Melan-A, Sox-10**) for each patient when there is a correct match 

**Step 3:**  Send matched slices to pathologist team, who will annotate H&E slice with high grade, low grade, and benign regions and send back to us

**Step 4:**  While the pathologist team annotates slices, orienting matched slices and apply patching across epithelium for matched slices. Currently looking at 3 different approaches to patching to find the optimal results

**Step 5:** Create a machine learning model to classify slices as low-grade, high-grade, or benign

**Literature Review**: Researched adaptive pooling, cross view transformer, square patches (advantages, sizes, and rationale), padding effect, etc.