GitBucket
MedSegAgent (https://ieeexplore.ieee.org/document/11455620) を再現するためのWebAppを実装
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GitHub
committed
on 4 Jun 2025
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| agents | 1 year ago | ||
| assets | 1 year ago | ||
| dataset | 1 year ago | ||
| prompts | 1 year ago | ||
| results | 1 year ago | ||
| utils | 1 year ago | ||
| .DS_Store | 1 year ago | ||
| .gitignore | 1 year ago | ||
| LICENSE | 1 year ago | ||
| OAI_CONFIG_LIST.example | 1 year ago | ||
| README.md | 1 year ago | ||
| eval_diff_llms.sh | 1 year ago | ||
| eval_example.sh | 1 year ago | ||
| evaluate.py | 1 year ago | ||
| model_selection_bench.jsonl | 1 year ago | ||
| model_selection_test_case.jsonl | 1 year ago | ||
| requirements.txt | 1 year ago | ||
README.md
Med-SegAgent: Universal & Scalable Multi-Agent System for Instructive Medical Image Segmentation
Key Features:
- Universal & Scalable: Tackle diverse medical image segmentation tasks using natural language instructions.
- Precise Automation: Automatically selects the most suitable segmentation models.
- Enhanced Robustness: Improves reliability through multi-model integration and ensemble capabilities.
Quick Start:
Setup your environment
conda create -n medsegagent python=3.12 conda activate medsegagent pip install uv uv pip install -r requirements.txt
Then set your LLM service API keys like OAI_CONFIG_LIST.example, the config file should be named OAI_CONFIG_LIST.
[
{
"model": "Qwen/Qwen2.5-32B-Instruct",
"api_key": "<Your API KEY>",
"base_url": "https://api.siliconflow.cn/v1",
"tags": ["silicon"]
},
{
"model": "gpt-4o-2024-08-06",
"api_key": "<Your API KEY>",
"base_url": "<your BASE URL>",
"tags": ["openai"]
}
]
Test script
Start from eval_example.sh to try our Coarse-to-Fine setting of seg model selection.
python ./evaluate.py \
--test_file_path "model_selection_test_case.jsonl" \
--test_pattern "C2F" \
--model "gpt-4o-2024-08-06" \
--log_to_file