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https://github.com/BenjaminTowle/SimSAM

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Host: GitHub
URL: https://github.com/BenjaminTowle/SimSAM
Owner: BenjaminTowle
License: mit
Created: 2023-11-19T08:23:07.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2024-06-04T13:46:02.000Z (7 months ago)
Last Synced: 2024-06-04T15:33:00.780Z (7 months ago)
Language: Python
Size: 369 KB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

        # SimSAM: Zero-shot Medical Image Segmentation via Simulated Interaction

[![arXiv](https://img.shields.io/badge/arXiv-2305.16852-b31b1b.svg)](https://arxiv.org/abs/2406.00663)

[![IEEE Paper](https://img.shields.io/badge/IEEE-Paper-blue)](https://ieeexplore.ieee.org/document/10635227)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

![simsam](assets/simsam_small.png)

Official code for the [ISBI 2024](https://biomedicalimaging.org/2024/) paper: *SimSAM: Zero-shot Medical Image Segmentation via Simulated Interaction*, which was awarded as a [top 12 oral paper](https://biomedicalimaging.org/2024/best-paper-award-finalists-announced/).

## Requirements

* datasets

* monai

* numpy

* opencv-python

* tifffile

* torch

* transformers

The relevant packages can be installed with:

```shell

pip install -r requirements.txt

```

This repo also uses [surface-distance](https://github.com/google-deepmind/surface-distance/tree/master), which should be downloaded and included in the root folder.

## Downloading the datasets

This paper makes use of three datasets, which need to be separately downloaded.

- Breast Ultrasound Scans can be downloaded from [Kaggle](https://www.kaggle.com/datasets/aryashah2k/breast-ultrasound-images-dataset).

- CVC ClinicDB is available [here](https://polyp.grand-challenge.org/CVCClinicDB/).

- ISIC 2016 is available [here](https://challenge.isic-archive.com/data/#2016) (you should download Task 1).

Downloaded content should be placed in the `data/` folder.

## Evaluation

You can evaluate either the `baseline` or the `simsam` model with the `eval.py` script, by selecting from one of the following datasets: `["busi", "cvc", "isic"]`. For example:

```shell

python scripts/eval.py --model_load_path facebook/sam-vit-base \

    --dataset cvc \

    --model_type simsam \

```

## Training

You can also fine-tune the SAM model on any of the datasets with:

```shell

python scripts/train.py --model_load_path facebook/sam-vit-base \

    --model_save_path path/to/save/model \

    --dataset cvc \

    --learning_rate 1e-5 \

    --num_train_epochs 10 \

```