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Combining Graph Neural Network and Mamba to Capture Local and Global Tissue Spatial Relationships in Whole Slide Images
Paper preprint
In computational pathology, extracting spatial features from gigapixel whole slide images (WSIs) is a fundamental task, but due to their large size, WSIs are typically segmented into smaller tiles. A critical aspect of this analysis is aggregating information from these tiles to make predictions at the WSI level. We introduce a model that combines a message-passing graph neural network (GNN) with a state space model (Mamba) to capture both local and global spatial relationships among the tiles in WSIs. The model's effectiveness was demonstrated in predicting progression-free survival among patients with early-stage lung adenocarcinomas (LUAD). We compared the model with other state-of-the-art methods for tile-level information aggregation in WSIs, including tile-level information summary statistics-based aggregation, multiple instance learning (MIL)-based aggregation, GNN-based aggregation, and GNN-transformer-based aggregation. Additional experiments showed the impact of different types of node features and different tile sampling strategies on the model performance. This work can be easily extended to any WSI-based analysis.
Instructions
Required packages
First, create a pytorch docker container using:
docker run --shm-size=2g --gpus all -it --rm -v /:/workspace -v /etc/localtime:/etc/localtime:ro nvcr.io/nvidia/pytorch:24.02-py3
Then install all packages listed here by running the following commands:
chmod +x pip_commands.sh
./pip_commands.sh
More information on the pytorch docker container nvcr.io/nvidia/pytorch:24.02-py3 can be found here(https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch/tags).
Preprocessing
If you would like to use the cohorts (NLST or TCGA), magnification level, and tile size used in our paper (either size 512 by 512 at 10x (1 mpp) or 1024 by 1024 at 20x (0.5 mpp) so that the total area covered by a tile is consistent across all patients which have different magnification levels available):
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Download NLST data from NLST, download TCGA data from TCGA-LUAD.
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Use generate_tiles.py to generate tiles by specifying the location of the input WSIs, and output tiles:
python generate_tiles.py --path_to_wsi_images <path_to_wsi_images> --path_to_generated_tiles <path_to_generated_tiles>
If you would like to use other cohorts, you will run generate_tiles_generic.py by specifying the WSI level and tile size, as well as the location of the input WSIs, and output tiles:
python generate_tiles_generic.py --wsi_level <wsi_level> --tile_size <tile_size> --path_to_wsi_images <path_to_wsi_images> --path_to_generated_tiles <path_to_generated_tiles>
Input data structure:
├── <patient_id>
│ ├── <slide_id1.svs>
│ ├── <slide_id2.svs>
│ ├── <...>
Output data structure:
├── <patient_id>
│ ├── <tiles_png>
│ ├──<tile_id1.png>
│ ├──<tile_id2.png>
│ ├──<...>
Feature extraction
First, request access to the pretrained UNI model weights here.
Then run main_uni_and_luad_subtype.py to extract all tile/node features.
CUDA_VISIBLE_DEVICES=0 python main_uni_and_luad_subtype.py --path_to_generated_tiles <path_to_generated_tiles> --path_to_extracted_features <path_to_extracted_features> --path_to_patient_outcome <path_to_patient_outcome>
path_to_generated_tiles is the parent path to the tiles generated from the previous module.
path_to_extracted_features is the parent path where the extracted features will be stored after running the script.
path_to_patient_outcome is the path to the csv file that contains three columns including patient IDs (pid), event status (event), and time to event or follow-up time in days (days).
There will be a prompt asking for your HuggingFace access token. You can go to Settings and then Access Tokens and copy the token by conch_uni once you got access to the UNI weights.
Output data structure:
├── <patient_id1.csv>
├── <patient_id2.csv>
├── <...>
where in each csv file, the first column is slide id, second column tile name, third column patient's time to event (or follow-up time), forth column patient's event status, fifth column the LUAD histologic subtype, and the rest of the 1024 columns are the UNI features.
Graph data construction
Run create_dataset_uni.py to get the procesed graph dataset object named graph_data.pt that can be fed into the graph modeling code in the next step, using the extracted features from the previous step as input.
python create_dataset_uni.py --path_to_extracted_features <path_to_extracted_features> --processed_graph_data_path <processed_graph_data_path>
path_to_extracted_features is the same as path_to_extracted_features from the previous module.
processed_graph_data_path is the parent path where the generated graph data object will be stored after running the script.
Output data structure:
├── <processed_graph_data_path>
├── <processed>
│ ├── <graph_data.pt>
│ ├── <pre_filter.pt>
│ ├── <pre_transform.pt>
Modeling
Run main_gat_mamba.py. This script allows one to do both training and inference on the data and print out the train, validation, and test set C-index and test set dynamic AUC.
CUDA_VISIBLE_DEVICES=0 python main_gat_mamba.py --graph_data_path <processed_graph_data_path> --cv_split_path <cv_split_path>
graph_data_path is the same path as processed_graph_data_path from the previous module.
cv_split_path is the parent path to all the 5-fold cross validation splits.
Splits are in the following structure:
├── <splits>
│ ├── <fold0>
│ ├──pids_train.csv
│ ├──pids_val.csv
│ ├──pids_test.csv
│ ├── <fold1>
│ ├──pids_train.csv
│ ├──pids_val.csv
│ ├──pids_test.csv
│ ├── <fold2>
│ ├──pids_train.csv
│ ├──pids_val.csv
│ ├──pids_test.csv
...
Each csv file contains at least one column named pid, the patient IDs.
Citation
@article{
ding2024combining,
title={Combining Graph Neural Network and Mamba to Capture Local and Global Tissue Spatial Relationships in Whole Slide Images},
author={Ruiwen Ding and Kha-Dinh Luong and Erika Rodriguez and Ana Cristina Araujo Lemos da Silva and William Hsu},
year={2024},
journal={arXiv preprint arXiv:2406.04377}
}