leoyinn/qwen3vl-flare25

Qwen3-VL-4B-FLARE25

Model Description

This is a fully finetuned version of Qwen/Qwen3-VL-4B-Instruct on the FLARE 2025 medical imaging dataset. The model has been trained to perform various medical vision-language tasks across 8 imaging modalities and 19 different datasets.

Base Model: Qwen3-VL-4B-Instruct Training Dataset: FLARE25 (Medical Imaging Foundation Models: A Multi-task Learning Framework) Training Samples: 12232 samples across 26 datasets Supported Tasks:

• Classification
• Multi-label Classification
• Detection
• Instance Detection
• Regression
• Counting
• Report Generation

Supported Medical Imaging Modalities

1. Ultrasound - Breast ultrasound, intrauterine growth charts
2. X-ray - Dental, chest, periapical radiographs
3. Retinography - Fundus imaging, diabetic retinopathy
4. Microscopy - Chromosome analysis, bone marrow, cell counting
5. Clinical Photography - Neonatal jaundice assessment
6. Dermatology - Skin lesion classification
7. Endoscopy - Gastrointestinal imaging
8. Mammography - Breast cancer screening

Performance Summary

Task	Primary Metric	Baseline	Finetuned	Improvement
Classification	Balanced Accuracy	2.2%	53.5%	+2,309%
Detection	F1@0.5	0.0%	80.3%	∞ (new capability)
Instance Detection	F1@0.5	0.01%	1.0%	+9,900%
Multi-label Classification	F1 Macro	28.3%	50.3%	+77.7%
Regression	MAE	35.8	22.4	+37.3%
Counting	MAE	417.7	244.4	+41.5%
Report Generation	GREEN Score	67.7%	80.8%	+19.4%

Usage

Please check our GitHub for details

Training Details

Training Hyperparameters

• Base Model: Qwen3-VL-4B-Instruct
• Training Framework: DeepSpeed ZeRO-3
• Learning Rate: 1e-5
• Batch Size: 4 per device
• Gradient Accumulation Steps: 4
• Training Epochs: 2
• Max Sequence Length: 8192
• Image Resolution: Dynamic (max_pixels: 50176, min_pixels: 784)
• Optimizer: AdamW
• Mixed Precision: BF16
• Gradient Checkpointing: Enabled

Training Data Distribution

The model was trained on 19 medical imaging datasets across 8 modalities:

Ultrasound:

• BUSI, BUS-UCLM (Classification)
• BUSI-det, BUS-UCLM-det (Detection)
• IUGC (Classification + Detection)

X-ray:

• Dental, Periapical, Bone Resorption, ChestDR, IU-XRay

Retinography:

• Retino, Fundus

Microscopy:

• Chromosome, Bone Marrow, NeurIPS22-Cell

Clinical/Dermatology/Endoscopy/Mammography:

• Neojaundice, BCN20000, Endo, CMMD

Citation

If you use this model, please cite:

@misc{qwen3vl-flare25,
  author = {Shuolin Yin},
  title = {Qwen3-VL-4B Finetuned on FLARE25 Medical Imaging Dataset},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/leoyinn/qwen3vl-flare25}}
}

@misc{qwen3technicalreport,
      title={Qwen3 Technical Report}, 
      author={Qwen Team},
      year={2025},
      eprint={2505.09388},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.09388}, 
}

License

This model is released under the Apache 2.0 License. The base model license from Qwen also applies.

Acknowledgments

• Base Model: Qwen3-VL-4B-Instruct by Alibaba Cloud
• Dataset: FLARE 2025 Medical Imaging Challenge
• Training Infrastructure: Built on the official Qwen3-VL finetuning framework

Repository

Full training code, evaluation scripts, and results: GitHub - FLARE25-QWen3VL-4B