As generative models like Stable Diffusion and CT-GAN become highly capable of generating hyper-realistic medical imagery, this model serves as a defensive mechanism to prevent synthetic injections into clinical databases and insurance workflows. By analyzing the latent space and edge consistencies of clinical scans, it outputs a structured forensic report including confidence scores and anomaly localization.

Developed by: oke39
Model type: Multimodal Vision-Language Model (PEFT/LoRA Adapter)
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model: google/medgemma-1.5-4b-it

Model Sources [optional]

Demo / UI Dashboard:
Kaggle Challenge: MedGemma Impact Challenge (Novel Task)

Uses

Direct Use

This adapter is intended to be applied over the base MedGemma 1.5 (4B) model. It is designed to process an input medical image tensor (CT/MRI) alongside a text prompt requesting a forensic analysis.

Out-of-Scope Use

This model is a proof-of-concept developed for a hackathon. It is not intended for live production clinical diagnosis or to replace certified radiological review. It is strictly designed for digital forensic verification of scan authenticity.

Bias, Risks, and Limitations

The model's accuracy is highly dependent on the resolution of the input scan. Downscaled or heavily compressed JPEGs may trigger false positives due to compression artifacts mimicking generative noise.
Current fine-tuning primarily targets Stable Diffusion and CT-GAN signatures; it may not generalize to entirely novel generative architectures without further training.

How to Get Started with the Model

To use this model, you must load the base model in 4-bit precision and apply this LoRA adapter using the peft library.

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "google/medgemma-1.5-4b-it"
adapter_id = "oke39/medgemma-4b-forensic-agent"

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(base_model_id, load_in_4bit=True)

# Apply Forensic LoRA Adapter
model = PeftModel.from_pretrained(base_model, adapter_id)
[More Information Needed]

### Downstream Use [optional]

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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### Framework versions

- PEFT 0.17.1