---
license: apache-2.0
base_model:
- google/medgemma-1.5-4b-it
- AlphaRaven/medgemma-4b-antihallu
tags:
- medical
- dermatology
- adverse-event-detection
- medgemma
---
# MedGemma AE Detection

MedGemma 1.5-4B fine-tuned for **visual adverse event detection** from clinical photographs using paired image comparison.

- **Base model**: `AlphaRaven/medgemma-4b-antihallu`
- **Method**: LoRA fine-tuning (r=8, alpha=16, target: q_proj/v_proj)
- **Training**: 100 epochs
- **Task**:
  - 1. Paired comparison of baseline vs. current patient photos.
    2. Classify skin AEs from patient photos into 21 categories (normal + 7 AE types × 3 CTCAE grades; alopecia G1-G2 only)
- **AE types**: maculopapular rash, acneiform rash, periorbital edema, SJS, stomatitis, pruritus, alopecia

## Performance (test set, 42 paired images):
    - Accuracy: 95.2%
    - Weighted F1: 0.951
    - 
## Dataset**: Synthetic clinical photographs (Gemini-generated)
    - 147 train / 21 val / 42 test paired images

## Usage

```python
from transformers import AutoModelForImageTextToText, AutoProcessor

model = AutoModelForImageTextToText.from_pretrained("AlphaRaven/medgemma-ae-detection")
processor = AutoProcessor.from_pretrained("google/medgemma-1.5-4b-it")
```

  Input: Two images (baseline photo + current photo) with a clinical reasoning prompt using CTCAE grading criteria.
  Output: JSON array of detected AEs with ae_term, grade, and reasoning.

Part of the [Clinical Trial Simulation Engine](https://github.com/AlphaRaven/ClinicalTrialEngine) pipeline.