efecelik
/

medgemma-brain-mri-lora

@@ -1,199 +1,154 @@
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 ---
+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
+  - brain-mri
+  - brain-tumor-classification
+  - hai-def
+  - neuroimaging
+datasets:
+  - sartajbhuvaji/Brain-Tumor-Classification
+language:
+  - en
+pipeline_tag: image-text-to-text
 ---
+# 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](https://huggingface.co/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](https://modal.com) 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
+```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-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](https://www.kaggle.com/competitions/med-gemma-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.