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 /
arXiv /
MLiNS Lab

Installation

1. Clone OpenSRH github repo
   console git clone git@github.com:MLNeurosurg/opensrh.git
2. Install miniconda: follow instructions
   here
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.