AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder
This work improves the Segment Anything Model (SAM) for medical image segmentation by replacing its conditioning mechanism with an image-based encoder. Without further fine-tuning SAM, this modification achieves state-of-the-art results on medical images and video benchmarks.
The paper associated with this repository can be found here.
We used the following datasets in our experiments:
To use AutoSAM, follow these steps:
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Clone the repository:
git clone https://github.com/your_username/AutoSAM.git cd AutoSAM/ -
conda:
conda create --name autosam python=3.10 pip install -r requirements.txt
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training:
python train.py