---
title: DermLIP_PanDerm-base-w-PubMed-256
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/DermLIP_PanDerm-base-w-PubMed-256"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/DermLIP_PanDerm-base-w-PubMed-256.md"
repository: AI-ModelScope/DermLIP_PanDerm-base-w-PubMed-256
last_updated: 2025-10-21
license: cc-by-4.0
parameters: 195.9M
tensor_type:
  - F32
library_name:
  - pytorch
  - safetensors
frameworks:
  - pytorch
language:
  - en
downloads: 150
stars: 0
tags:
  - vision
  - image-text-to-text
  - medical
  - dermatology
  - multimodal
  - clip
  - zero-shot-classification
  - image-classification
---

# DermLIP_PanDerm-base-w-PubMed-256

> DermLIP_PanDerm-base-w-PubMed-256 - AI-ModelScope 在 ModelScope 开源的模型。DermLIP: Dermatology Language-Image Pretraining

AI-ModelScope/DermLIP_PanDerm-base-w-PubMed-256 是 ModelScope 魔搭社区上的 195.9M 参数机器学习模型，采用 cc-by-4.0 许可。

- **Repository**: AI-ModelScope/DermLIP_PanDerm-base-w-PubMed-256
- **License**: cc-by-4.0
- **Parameters**: 195.9M
- **Tags**: vision, image-text-to-text, medical, dermatology, multimodal, clip, zero-shot-classification, image-classification
- **Downloads**: 150
- **Stars**: 0
- **Last updated**: 2025-10-21

Source: https://www.modelscope.cn/models/AI-ModelScope/DermLIP_PanDerm-base-w-PubMed-256

---

# DermLIP: Dermatology Language-Image Pretraining

## Model Description

**DermLIP** is a vision-language model for dermatology, trained on the **Derm1M** dataset—the largest dermatological image-text corpus to date. This model variant (`PanDerm-base-w-PubMed-256`) utilizes domain-specific pretraining to deliver superior performance compared to other DermLIP variants..

### Model Details

- **Model Type:** Pretrained Vision-Language Model (CLIP-style)

- **Architecture:**

  - **Vision encoder (PanDerm-base)**: https://github.com/SiyuanYan1/PanDerm
  - **Text encoder (PubmedBert-256)**: https://huggingface.co/NeuML/pubmedbert-base-embeddings

- **Resolution:** 224×224 pixels

- **Paper:** https://arxiv.org/abs/2503.14911

- **Repository:** https://github.com/SiyuanYan1/Derm1M

- **license:** cc-by-nc-nd-4.0


## Training Details

- **Training data:** 403,563 skin image-text pairs from Derm1M datasets. Images include both dermoscopic and clinical images.
- **Training objective:** image-text contrastive loss
- **Hardware:** 1 x Nvidia H200（～90GB memory usage）
- **Hours used:**  ~9.5 hours
  
## Intended Uses

### Primary Use Cases

- Zero-shot classification
- Few-shot learning
- Cross-modal retrieval
- Concept annotation/explanation


## How to Use

### Installation

First, clone the Derm1M repository:
```bash
git clone git@github.com:SiyuanYan1/Derm1M.git
cd Derm1M
```

Then install the package following the instruction in the repository.

### Quick Start
```python
import open_clip
from PIL import Image
import torch

# Load model with huggingface checkpoint
model, _, preprocess = open_clip.create_model_and_transforms(
    'hf-hub:redlessone/DermLIP_PanDerm-base-w-PubMed-256'
)
model.eval()

# Initialize tokenizer
tokenizer = open_clip.get_tokenizer('hf-hub:redlessone/DermLIP_PanDerm-base-w-PubMed-256')

# Read example image
image = preprocess(Image.open("your_skin_image.png")).unsqueeze(0)

# Define disease labels (example: PAD dataset classes)
PAD_CLASSNAMES = [
    "nevus",
    "basal cell carcinoma",
    "actinic keratosis",
    "seborrheic keratosis",
    "squamous cell carcinoma",
    "melanoma"
]

# Build text prompts
template = lambda c: f'This is a skin image of {c}'
text = tokenizer([template(c) for c in PAD_CLASSNAMES])

# Inference
with torch.no_grad(), torch.autocast("cuda"):
    # Encode image and text
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    
    # Normalize features
    image_features /= image_features.norm(dim=-1, keepdim=True)
    text_features /= text_features.norm(dim=-1, keepdim=True)
    
    # Compute similarity
    text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)

# Get prediction
final_prediction = PAD_CLASSNAMES[torch.argmax(text_probs[0])]
print(f'This image is diagnosed as {final_prediction}.')
print("Label probabilities:", text_probs)
```

## Contact

For any additional questions or comments, contact Siyuan Yan (`siyuan.yan@monash.edu`), 

## Cite our Paper
```bibtex
@misc{yan2025derm1m,
  title        = {Derm1M: A Million‑Scale Vision‑Language Dataset Aligned with Clinical Ontology Knowledge for Dermatology},
  author       = {Siyuan Yan and Ming Hu and Yiwen Jiang and Xieji Li and Hao Fei and Philipp Tschandl and Harald Kittler and Zongyuan Ge},
  year         = {2025},
  eprint       = {2503.14911},
  archivePrefix= {arXiv},
  primaryClass = {cs.CV},
  url          = {https://arxiv.org/abs/2503.14911}
}

@article{yan2025multimodal,
  title={A multimodal vision foundation model for clinical dermatology},
  author={Yan, Siyuan and Yu, Zhen and Primiero, Clare and Vico-Alonso, Cristina and Wang, Zhonghua and Yang, Litao and Tschandl, Philipp and Hu, Ming and Ju, Lie and Tan, Gin and others},
  journal={Nature Medicine},
  pages={1--12},
  year={2025},
  publisher={Nature Publishing Group}
}
```