---
library_name: peft
license: other
license_name: health-ai-developer-foundations
license_link: https://developers.google.com/health-ai-developer-foundations/terms
base_model: google/medgemma-4b-it
tags:
  - medgemma
  - lora
  - medical-ai
  - chest-xray
  - thoracic-disease
  - hai-def
  - radiology
datasets:
  - BahaaEldin0/NIH-Chest-Xray-14
language:
  - en
pipeline_tag: image-text-to-text
---

# MedGemma Chest X-Ray LoRA

**Thoracic disease classification adapter fine-tuned on NIH ChestX-ray14 using MedGemma 4B.**

Identifies 14 thoracic pathologies plus "No Finding" from frontal chest radiographs using multi-label classification.

## Model Details

| Property | Value |
|----------|-------|
| **Base Model** | [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it) |
| **Method** | LoRA (Low-Rank Adaptation) |
| **Task** | Multi-label thoracic disease classification (15 labels) |
| **Modality** | Chest X-ray (frontal/PA view) |
| **Framework** | PyTorch + HuggingFace Transformers + PEFT |

## Training Dataset

**[NIH ChestX-ray14](https://huggingface.co/datasets/BahaaEldin0/NIH-Chest-Xray-14)** — 112K frontal chest X-rays with 14 disease labels.

Reference: Wang et al. 2017, CVPR - "ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks"

- **Train samples:** 10,000 (curated subset from 112K)
- **Validation samples:** 1,000

### Pathology Labels (15 classes)

| Label | Description |
|-------|-------------|
| No Finding | No acute cardiopulmonary abnormality |
| Atelectasis | Partial or complete lung collapse |
| Cardiomegaly | Enlarged heart (cardiothoracic ratio > 0.5) |
| Effusion | Fluid in the pleural space |
| Infiltration | Opacity suggesting infection or inflammation |
| Mass | Solid lesion > 3cm, requires malignancy evaluation |
| Nodule | Focal opacity < 3cm |
| Pneumonia | Infectious consolidation with air bronchograms |
| Pneumothorax | Air in pleural space causing lung collapse |
| Consolidation | Dense opacification replacing air with fluid/pus/cells |
| Edema | Fluid in lung interstitium/alveoli (often from heart failure) |
| Emphysema | Hyperinflation with flattened diaphragms |
| Fibrosis | Reticular opacities with volume loss |
| Pleural_Thickening | Increased pleural surface density |
| Hernia | Abdominal contents in thoracic cavity |

> **Note:** Multi-label classification — multiple pathologies can co-occur in a single image.

## Training Configuration

### LoRA Parameters

| Parameter | Value |
|-----------|-------|
| Rank (r) | 16 |
| Alpha | 32 |
| Dropout | 0.05 |
| Target Modules | all-linear |
| Task Type | CAUSAL_LM |
| Trainable Params | 1.38B / 5.68B (24.3%) |

### Hyperparameters

| Parameter | Value |
|-----------|-------|
| Epochs | 1 |
| Per-device Batch Size | 1 |
| Gradient Accumulation Steps | 8 (effective batch = 8) |
| Learning Rate | 2e-4 |
| LR Scheduler | Linear with warmup |
| Warmup Ratio | 0.03 |
| Max Grad Norm | 0.3 |
| Precision | bfloat16 |
| Gradient Checkpointing | Enabled |
| Seed | 42 |

### Infrastructure

| Property | Value |
|----------|-------|
| GPU | NVIDIA L4 (24 GB VRAM) |
| Cloud Platform | [Modal](https://modal.com) serverless GPU |
| Training Time | ~60-90 minutes |

## Prompt Format

**Input:**
> Analyze this chest X-ray and identify any findings.

**Output:**
> This chest X-ray shows **Pneumonia**.
>
> Pneumonia (infectious consolidation of lung parenchyma with air bronchograms).

## Usage

```python
from transformers import AutoProcessor, AutoModelForImageTextToText
from peft import PeftModel
from PIL import Image

base_model_id = "google/medgemma-4b-it"
adapter_id = "efecelik/medgemma-chest-xray-lora"

processor = AutoProcessor.from_pretrained(base_model_id)
model = AutoModelForImageTextToText.from_pretrained(
    base_model_id, torch_dtype="bfloat16", device_map="auto"
)
model = PeftModel.from_pretrained(model, adapter_id)

image = Image.open("chest_xray.jpg").convert("RGB")
messages = [
    {"role": "user", "content": [
        {"type": "image"},
        {"type": "text", "text": "Analyze this chest X-ray and identify any findings."}
    ]}
]

inputs = processor.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=True,
    return_dict=True, return_tensors="pt", images=[image]
).to(model.device)

output = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(output[0], skip_special_tokens=True))
```

## Intended Use

This adapter is part of the **MedVision AI** platform built for the [MedGemma Impact Challenge](https://www.kaggle.com/competitions/med-gemma-impact-challenge). It is designed for:

- **Medical education**: Helping students learn systematic chest X-ray interpretation
- **Clinical decision support**: Assisting radiologists with thoracic disease screening
- **Research**: Exploring fine-tuned medical VLMs for chest radiography

## Limitations

- **Not for clinical diagnosis.** This model is for educational and research purposes only.
- **Label noise:** NIH ChestX-ray14 labels were NLP-extracted from reports and contain noise (~10-30% error rate depending on pathology).
- **Curated subset:** Trained on 10K of 112K available images.
- **Single view:** Trained on frontal views only. Lateral views not included.
- **Single epoch:** Trained for 1 epoch; further training may improve performance.

## Citation

```bibtex
@inproceedings{wang2017chestx,
  title={Chestx-ray8: Hospital-scale chest x-ray database and benchmarks},
  author={Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and Bagheri, Mohammadhadi and Summers, Ronald M},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2097--2106},
  year={2017}
}
```

## Disclaimer

This model is for **educational and research purposes only**. It is NOT intended for clinical diagnosis or patient care decisions. Always consult qualified medical professionals for medical advice.