---
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
  - abdominal-ct
  - organ-classification
  - hai-def
  - medmnist
datasets:
  - satwatbashir/organamnist
language:
  - en
pipeline_tag: image-text-to-text
---

# MedGemma Abdominal CT LoRA

**Abdominal organ classification adapter fine-tuned on OrganAMNIST (MedMNIST) using MedGemma 4B.**

Identifies the primary organ or anatomical structure visible in abdominal CT axial slices across 11 classes.

## Model Details

| Property | Value |
|----------|-------|
| **Base Model** | [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it) |
| **Method** | LoRA (Low-Rank Adaptation) |
| **Task** | Multi-class organ classification (11 classes) |
| **Modality** | Abdominal CT (axial 2D slices) |
| **Framework** | PyTorch + HuggingFace Transformers + PEFT |

## Training Dataset

**[OrganAMNIST](https://huggingface.co/datasets/satwatbashir/organamnist)** from the MedMNIST v2 benchmark — standardized 2D axial CT slices for organ classification.

Reference: Yang et al. 2023, *Scientific Data* - "MedMNIST v2: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification"

- **Original dataset:** ~58,850 images
- **Train samples:** 10,000 (curated subset)
- **Validation samples:** 1,000
- **Image size:** 28x28 pixels (MedMNIST standard, resized by processor)

### Class Distribution

| ID | Organ | Description |
|:---:|-------|-------------|
| 0 | Bladder | Urinary bladder in the pelvis |
| 1 | Femur (left) | Proximal left femur and femoral head |
| 2 | Femur (right) | Proximal right femur and femoral head |
| 3 | Heart | Cardiac silhouette with chambers and great vessels |
| 4 | Kidney (left) | Left kidney with cortex and medulla |
| 5 | Kidney (right) | Right kidney (slightly lower due to liver) |
| 6 | Liver | Largest solid abdominal organ, right upper quadrant |
| 7 | Lung (left) | Left hemithorax pulmonary tissue |
| 8 | Lung (right) | Right hemithorax, three lobes |
| 9 | Spleen | Left upper quadrant, posterior to stomach |
| 10 | Pancreas | Retroperitoneal organ crossing midline |

## 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 | ~45-60 minutes |

## Prompt Format

**Input:**
> Identify the primary organ or structure visible in this abdominal CT slice.

**Output:**
> This abdominal CT slice primarily shows the **Liver**.
>
> Liver (largest solid organ in the abdomen, occupying the right upper quadrant with homogeneous parenchymal density).

## 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-abdominal-ct-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("abdominal_ct.jpg").convert("RGB")
messages = [
    {"role": "user", "content": [
        {"type": "image"},
        {"type": "text", "text": "Identify the primary organ or structure visible in this abdominal CT slice."}
    ]}
]

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 abdominal CT anatomy and organ identification
- **Clinical decision support**: Assisting radiologists with organ localization
- **Research**: Exploring fine-tuned medical VLMs for abdominal imaging

## Limitations

- **Not for clinical diagnosis.** This model is for educational and research purposes only.
- **Organ identification only:** Classifies visible organ, does not detect pathology within organs.
- **Low resolution source:** MedMNIST images are 28x28 pixels, limiting fine structural detail.
- **Normal anatomy only:** Trained on healthy organ appearances, not pathological variants.
- **Single epoch:** Trained for 1 epoch; further training may improve performance.

## Citation

```bibtex
@article{yang2023medmnist,
  title={MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification},
  author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing},
  journal={Scientific Data},
  volume={10},
  number={1},
  pages={41},
  year={2023},
  publisher={Nature Publishing Group UK London}
}
```

## 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.