# OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology

Companion code for OpenSRH. Paper submitted to NeurIPS 2022

Datasets and Benchmarks Track.

[**Paper Website**](https://opensrh.mlins.org) /

[**arXiv**](https://arxiv.org/abs/2206.08439) /

[**MLiNS Lab**](https://mlins.org)

## Installation

1. Clone OpenSRH github repo

   ```console

   git clone git@github.com:MLNeurosurg/opensrh.git

   ```

2. Install miniconda: follow instructions

    [here](https://docs.conda.io/en/latest/miniconda.html)

3. Create conda environment

    ```console

    conda create -n opensrh python=3.9

    ```

4. Activate conda environment

    ```console

    conda activate opensrh

    ```

5. Install package and dependencies

    ```console

    <cd /path/to/opensrh/repo/dir>

    pip install -e .

    ```

## Directory organization

- opensrh: the library for training with OpenSRH

    - datasets: PyTorch datasets to work with the data release

    - losses: loss functions for contrastive learning

    - models: PyTorch networks for training and evaluation

    - train: training and evaluation scrpits

- README.md

- LICENSE

# Training / evaluation instructions

The code base is written using PyTorch Lightning, with custom network and

datasets.

## Cross entropy experiments

1. Download and uncompress the data.

2. Update the sample config file in `train/config/train_ce.yaml` with desired

    configurations.

3. Change directory to `train` and activate the conda virtual environment.

4. Use `train/train_ce.py` to start training:

    ```console

    python train_ce.py -c config/train_ce.yaml

    ```

## Contrastive learning experiments

1. Download and uncompress the data.

2. Update the sample config file in `train/config/train_contrastive.yaml` with

    desired configurations.

3. Change directory to `train` and activate the conda virtual environment.

4. Use `train/train_contrastive.py` to start training:

    ```console

    python train_contrastive.py -c config/train_contrastive.yaml

    ```

5. To run linear or finetuning protocol, update the config file

    `train/config/train_finetune.yaml` and continue training using

    `train/train_finetune.py`:

    ```console

    python train_finetune.py -c config/train_finetune.yaml

    ```

## Model evaluation

1. Update the sample config file in `train/config/eval.yaml` with desired

    configurations, including the PyTorch Lightning checkpoint you would like

    to use.

2. Change directory to `train` and activate the conda virtual environment.

3. Use `train/train_ce.py` to start training:

    ```console

    python eval.py -c config/eval.yaml

    ```

## License Information

OpenSRH data is released under Attribution-NonCommercial-ShareAlike 4.0

International (CC BY-NC-SA 4.0), and the code is licensed under the MIT License.

See LICENSE for license information and THIRD\_PARTY for third party notices.