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Overview

The midterm project is your opportunity to apply what you’ve learned to a clinical AI problem end-to-end. Choose one of two tracks based on your interests and background. Each track produces the same deliverables: working code and a mini field guide.

Deliverables:

1. Working code (Jupyter notebook or Python script)
2. Mini field guide (2-3 pages)
3. Brief presentation (5-7 minutes)

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Project Tracks

Choose ONE of the following tracks:

Track A: Medical Imaging

Task: Build a classification or segmentation model for medical images.

Suggested Datasets:

• NIH ChestX-ray14 — 112k chest X-rays, 14 disease labels
• ISIC Skin Lesion — Dermoscopy images with lesion masks
• APTOS Diabetic Retinopathy — Retinal images, DR grading
• Any MONAI dataset from the Model Zoo

Minimum Requirements:

• Train/val/test split with proper evaluation
• At least one baseline and one deep learning model
• Appropriate metrics (ROC/AUC for classification, Dice for segmentation)
• Visualization of predictions

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Track B: Structured Clinical Data

Task: Build a risk prediction or outcome model using tabular clinical data.

Suggested Datasets:

• MIMIC-III — ICU data (requires credentialing)
• eICU — Multi-center ICU data
• Heart Disease UCI — Classic prediction task
• Diabetes 130-US Hospitals — Readmission prediction

Minimum Requirements:

• Proper EDA and handling of missing data
• At least one baseline and one ML model (e.g., XGBoost, Random Forest)
• Appropriate metrics (AUC, calibration plot)
• Feature importance analysis (SHAP or similar)

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Custom Project (Requires Approval)

Have a different clinical AI project in mind? Propose it!

To get approval:

1. Email the instructor by March 20 with:
   • Dataset description and access plan
   • Clinical problem and why it matters
   • Proposed approach
2. Wait for approval before starting

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Requirements

Code (50 points)

Component	Points
Data loading and preprocessing	10
Model architecture (appropriate for task)	10
Training pipeline (loss, optimizer, logging)	10
Evaluation metrics (appropriate for task)	10
Code quality (readable, documented)	10

Mini Field Guide (35 points)

Write a 2-3 page document covering:

Section	Points
Problem Statement — What clinical problem does this address?	5
Data Description — What data did you use? Limitations?	5
Methods — What approach did you take and why?	10
Results — Key performance metrics with interpretation	10
Limitations & Next Steps — What would you do differently?	5

Presentation (15 points)

5-7 minute presentation covering:

• The problem and why it matters
• Your approach
• Key results
• One thing you learned

Presentations will be during class on April 1.

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Submission

Submit via GitHub (link TBD):

1. code/ — Your notebooks and/or scripts
2. field_guide.pdf — Your mini field guide
3. slides.pdf — Presentation slides
4. README.md — How to run your code

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Timeline

Date	Milestone
Mar 18	Project released, choose your track
Mar 20	Deadline for custom project proposals
Mar 25	Recommended: Data loaded, baseline running
Mar 31	In-class work session
Apr 1	Presentations in class
Apr 1	Code + Field Guide due by 11:59 PM

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Tips

• Start with the data — Make sure you can load and visualize before building models
• Get a baseline working first — A simple model that runs beats a complex model that doesn’t
• Document as you go — Don’t leave the field guide until the last minute
• It’s okay to fail — If your model doesn’t work well, analyze why. That’s valuable!
• Use what you learned — HW2 (EDA), HW3 (DICOM), HW4 (ML), HW5 (deep learning) are all relevant

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Grading Rubric Summary

Component	Points
Code	50
Mini Field Guide	35
Presentation	15
Total	100

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Resources

• MONAI Tutorials
• PyTorch Image Classification Tutorial
• Papers With Code - Medical Imaging
• Office hours — Use them!