efecelik/medgemma-brain-mri-lora

MedGemma Brain MRI LoRA

Brain tumor classification adapter fine-tuned on Brain Tumor MRI dataset using MedGemma 4B.

Classifies brain MRI images into 4 categories: glioma, meningioma, pituitary tumor, or no tumor.

Model Details

Property	Value
Base Model	google/medgemma-4b-it
Method	LoRA (Low-Rank Adaptation)
Task	Multi-class brain tumor classification (4 classes)
Modality	Brain MRI
Framework	PyTorch + HuggingFace Transformers + PEFT

Training Dataset

Brain Tumor MRI Classification dataset — multi-source brain MRI collection for tumor type classification.

Fallback sources tried in order: masoudnickparvar/brain-tumor-mri-dataset, AIOmarRehan/Brain_Tumor_MRI_Dataset, sartajbhuvaji/Brain-Tumor-Classification

• Train samples: ~5,700 (85% split)
• Validation samples: ~1,000 (15% split)
• Split strategy: train_test_split(test_size=0.15, seed=42)

Class Distribution

Label	Description
glioma	Malignant tumor from glial cells. Irregular, heterogeneous mass with surrounding edema. Most common primary malignant brain tumor.
meningioma	Typically benign tumor from the meninges. Well-defined, homogeneously enhancing extra-axial mass with dural tail sign.
pituitary	Adenoma from the pituitary gland in the sella turcica. May compress the optic chiasm causing visual field defects.
notumor	Normal brain MRI without intracranial mass, hemorrhage, or significant abnormality.

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 serverless GPU
Training Time	~30-45 minutes
Final Training Loss	0.1026

Prompt Format

Input:

Analyze this brain MRI and classify the finding.

Output:

This brain MRI shows Meningioma.

Meningioma (typically benign tumor arising from the meninges. Appears as a well-defined, homogeneously enhancing extra-axial mass, often with a dural tail sign).

Usage

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

base_model_id = "google/medgemma-4b-it"
adapter_id = "efecelik/medgemma-brain-mri-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("brain_mri.jpg").convert("RGB")
messages = [
    {"role": "user", "content": [
        {"type": "image"},
        {"type": "text", "text": "Analyze this brain MRI and classify the finding."}
    ]}
]

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. It is designed for:

• Medical education: Helping students learn brain tumor identification on MRI
• Clinical decision support: Assisting radiologists with brain lesion characterization
• Research: Exploring fine-tuned medical VLMs for neuroimaging

Limitations

• Not for clinical diagnosis. This model is for educational and research purposes only.
• Limited tumor types: Only classifies 4 categories. Many brain pathologies (abscess, stroke, MS) are not covered.
• Single sequence: Trained on individual MRI slices, not full 3D volumes or multi-sequence protocols.
• Single epoch: Trained for 1 epoch; further training may improve performance.

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.