Fine-Grained Visual Classification on HAM10000

Project Page: SelfSynthX.

Paper on arXiv: Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data

This model is a fine-tuned multimodal foundation model developed on the LLaVA-1.5-7B-hf base, optimized for fine-grained skin lesion classification and explainability using the HAM10000 dataset.

Key Details

Base Model: LLaVA-1.5-7B
Dataset: HAM10000
Innovation:
- Self-Synthesized Data: Generates interpretable explanations by extracting lesion-specific visual concepts using the Information Bottleneck principle.
- Iterative Fine-Tuning: Uses reward model-free rejection sampling to progressively improve classification accuracy and explanation quality.
Intended Use: Skin lesion classification with human-verifiable explanations for dermatological analysis.

How to Use

import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration

model_id = "YuchengShi/LLaVA-v1.5-7B-HAM10000"
model = LlavaForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True,
).to("cuda")
processor = AutoProcessor.from_pretrained(model_id)

conversation = [
    {
      "role": "user",
      "content": [
          {"type": "text", "text": "What type of skin lesion is this?"},
          {"type": "image"},
        ],
    },
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
image_file = "ham10000/test1.png"
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to("cuda", torch.float16)

output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))

Training & Evaluation

Training: Fine-tuned using LoRA on HAM10000 with iterative rejection sampling.
Evaluation: Demonstrates higher accuracy and robust, interpretable explanations compared to baseline models.

Citation

If you use this model, please cite:

@inproceedings{
  shi2025enhancing,
  title={Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data},
  author={Yucheng Shi and Quanzheng Li and Jin Sun and Xiang Li and Ninghao Liu},
  booktitle={The Thirteenth International Conference on Learning Representations},
  year={2025},
  url={https://openreview.net/forum?id=lHbLpwbEyt}
}

Contact

For any questions, suggestions, or issues, please open an issue on GitHub or contact us at yucheng.shi@uga.edu.

Github repository: https://github.com/sycny/SelfSynthX