efecelik
/

medgemma-abdominal-ct-lora

@@ -1,199 +1,177 @@
 ---
-library_name: transformers
-tags: []
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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
+  - 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.