https://github.com/mileswyn/SAMIHS

Code for our Paper "SAMIHS: Adaptation of Segment Anything Model for Efficient Intracranial Hemorrhage Segmentation".
https://github.com/mileswyn/SAMIHS

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Code for our Paper "SAMIHS: Adaptation of Segment Anything Model for Efficient Intracranial Hemorrhage Segmentation".

• Host: GitHub
• URL: https://github.com/mileswyn/SAMIHS
• Owner: mileswyn
• License: mit
• Created: 2023-11-09T07:26:33.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-06T02:51:50.000Z (12 months ago)
• Last Synced: 2024-02-06T03:36:33.182Z (12 months ago)
• Language: Python
• Size: 1.47 MB
• Stars: 20
• Watchers: 2
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-Segment-Anything - [code

README

        
## [SAMIHS: Adaptation of Segment Anything Model for Efficient Intracranial Hemorrhage Segmentation]

#### by Yinuo Wang, Kai Chen, Weimin Yuan, Cai Meng, Xiangzhi Bai

This repository provides a PyTorch implementation of our work accepted by ISBI 2024 --> [[**arXiv**]](https://arxiv.org/abs/2311.08190)

## Overview

- **Model**: SAMIHS: A parameter-efficient fine-tuning (PEFT) method 

- **Task**: To adapt the Segment Anything Model (SAM) to intracranial hemorrhage segmentation.

- **Ideas**: The parameter-refactoring adapters and boundary-sensitive loss are incorporated in SAMIHS to improve both efficiency and accuracy.



## Updates

- 2023.11.13: Code released.

## Usage

### 1. Installation

```bash

$ git clone https://github.com/mileswyn/SAMIHS.git

$ cd SAMIHS/

$ pip install requirements.txt

```

### 2. Checkpoints

We use checkpoint of SAM in [`vit_b`](https://github.com/facebookresearch/segment-anything) version. Please download the pre-trained model and place it at `pretrained/sam_vit_b_01ec64.pth`.

### 3. Data

- We have evaluated our method on two publicly-available datasets: [BCIHM](https://physionet.org/content/ct-ich/1.3.1/) [Instance](https://instance.grand-challenge.org/).

- After downloading the datasets, you can follow the `utils/preprocess.py` to save the slice in `.npy` format, and read them with the information in path `dataset/excel/`.

- The relevant information of your data should be set in [./utils/config.py](https://github.com/mileswyn/SAMIHS/blob/main/utils/config.py) .

### 4. Training

If you have already arranged your data, you can start training your model.

```

cd "/home/...  .../SAMIHS/"

python train.py -task  -sam_ckpt  -fold 

```

### 5. Testing

After finishing training, you can start testing your model.

```

python test.py -task  -sam_ckpt  -fold 

```

Before testing, don't forget modify the "load_path" (the path of your trained model) in [./utils/config.py].

## Citation

If our SAMIHS is helpful to you, please consider citing our [paper](https://arxiv.org/abs/2311.08190):

```

@article{wang2023samihs,

  title={SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentation},

  author={Wang, Yinuo and Chen, Kai and Yuan, Weimin and Meng, Cai and Bai, XiangZhi},

  journal={arXiv preprint arXiv:2311.08190},

  year={2023}

}

```

## Acknowledgement

- A lot of code is modified from [SAMUS](https://github.com/xianlin7/SAMUS).