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https://github.com/mahmoodlab/pathomicfusion

Fusing Histology and Genomics via Deep Learning - IEEE TMI
https://github.com/mahmoodlab/pathomicfusion

computational-pathogenomics fusion genomics histopathology mahmoodlab multimodal multimodal-network pathology pathomic transcriptomics

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Fusing Histology and Genomics via Deep Learning - IEEE TMI

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README 

        
# Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Diagnosis and Prognosis



  Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis, IEEE Transactions on Medical Imaging, 2020.

  [HTML]

  [arXiv]

  [Talk]

  
Richard J Chen, Ming Y Lu, Jingwen Wang, Drew FK Williamson, Scott J Rodig, Neal I Lindeman, Faisal Mahmood



```bash

@article{chen2020pathomic,

  title={Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis},

  author={Chen, Richard J and Lu, Ming Y and Wang, Jingwen and Williamson, Drew FK and Rodig, Scott J and Lindeman, Neal I and Mahmood, Faisal},

  journal={IEEE Transactions on Medical Imaging},

  year={2020},

  publisher={IEEE}

}

```



**Summary:** We propose a simple and scalable method for integrating histology images and -omic data using attention gating and tensor fusion. Histopathology images can be processed using CNNs or GCNs for parameter efficiency or a combination of the the two. The setup is adaptable for integrating multiple -omic modalities with histopathology and can be used for improved diagnostic, prognostic and therapeutic response determinations. 






## Community / Follow-Up Work :)



GitHub Repositories / Projects



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## Updates

* 05/26/2021: Updated Google Drive with all models and processed data for TCGA-GBMLGG and TCGA-KIRC. found using the [following link](https://drive.google.com/drive/u/1/folders/1swiMrz84V3iuzk8x99vGIBd5FCVncOlf). The data made available for TCGA-GBMLGG are the **same ROIs** used by [Mobadersany et al.](https://github.com/PathologyDataScience/SCNN)



## Setup



### Prerequisites

- Linux (Tested on Ubuntu 18.04)

- NVIDIA GPU (Tested on Nvidia GeForce RTX 2080 Tis on local workstations, and Nvidia V100s using Google Cloud)

- CUDA + cuDNN (Tested on CUDA 10.1 and cuDNN 7.5. CPU mode and CUDA without CuDNN may work with minimal modification, but untested.)

- torch>=1.1.0

- torch_geometric=1.3.0



## Code Base Structure

The code base structure is explained below: 

- **train_cv.py**: Cross-validation script for training unimodal and multimodal networks. This script will save evaluation metrics and predictions on the train + test split for each epoch on every split in **checkpoints**.

- **test_cv.py**: Script for testing unimodal and unimodal networks on only the test split.

- **train_test.py**: Contains the definitions for "train" and "test". 

- **networks.py**: Contains PyTorch model definitions for all unimodal and multimodal network.

- **fusion.py**: Contains PyTorch model definitions for fusion.

- **data_loaders.py**: Contains the PyTorch DatasetLoader definition for loading multimodal data.

- **options.py**: Contains all the options for the argparser.

- **make_splits.py**: Script for generating a pickle file that saves + aligns the path for multimodal data for cross-validation.

- **run_cox_baselines.py**: Script for running Cox baselines.

- **utils.py**: Contains definitions for collating, survival loss functions, data preprocessing, evaluation, figure plotting, etc...



The directory structure for your multimodal dataset should look similar to the following:

```bash

./

├── data

      └── PROJECT

            ├── INPUT A (e.g. Image)

                ├── image_001.png

                ├── image_002.png

                ├── ...

            ├── INPUT B (e.g. Graph)

                ├── image_001.pkl

                ├── image_002.pkl

                ├── ...

            └── INPUT C (e.g. Genomic)

                └── genomic_data.csv

└── checkpoints

        └── PROJECT

            ├── TASK X (e.g. Survival Analysis)

                ├── path

                    ├── ...

                ├── ...

            └── TASK Y (e.g. Grade Classification)

                ├── path

                    ├── ...

                ├── ...

```



Depending on which modalities you are interested in combining, you must: (1) write your own function for aligning multimodal data in **make_splits.py**, (2) create your DatasetLoader in **data_loaders.py**, (3) modify the **options.py** for your data and task. Models will be saved to the **checkpoints** directory, with each model for each task saved in its own directory. At the moment, the only supervised learning tasks implemented are survival outcome prediction and grade classification.



## Training and Evaluation

Here are example commands for training unimodal + multimodal networks.



### Survival Model for Input A

Example shown below for training a survival model for mode A and saving the model checkpoints + predictions at the end of each split. In this example, we would create a folder called "CNN_A" in "./checkpoints/example/" for all the models in cross-validation. It assumes that "A" is defined as a mode in **dataset_loaders.py** for handling modality-specific data-preprocessing steps (random crop + flip + jittering for images), and that there is a network defined for input A in **networks.py**. "surv" is already defined as a task for training networks for survival analysis in **options.py, networks.py, train_test.py, train_cv.py**.



```

python train_cv.py --exp_name surv --dataroot ./data/example/ --checkpoints_dir ./checkpoints/example/ --task surv --mode A --model_name CNN_A --niter 0 --niter_decay 50 --batch_size 64 --reg_type none --init_type max --lr 0.002 --weight_decay 4e-4 --gpu_ids 0

```

To obtain test predictions on only the test splits in your cross-validation, you can replace "train_cv" with "test_cv".

```

python test_cv.py --exp_name surv --dataroot ./data/example/ --checkpoints_dir ./checkpoints/example/ --task surv --mode input_A --model input_A_CNN --niter 0 --niter_decay 50 --batch_size 64 --reg_type none --init_type max --lr 0.002 --weight_decay 4e-4 --gpu_ids 0

```



### Grade Classification Model for Input A + B

Example shown below for training a grade classification model for fusing modes A and B. Similar to the previous example, we would create a folder called "Fusion_AB" in "./checkpoints/example/" for all the models in cross-validation. It assumes that "AB" is defined as a mode in **dataset_loaders.py** for handling multiple inputs A and B at the same time. "grad" is already defined as a task for training networks for grade classification in **options.py, networks.py, train_test.py, train_cv.py**.

```

python train_cv.py --exp_name surv --dataroot ./data/example/ --checkpoints_dir ./checkpoints/example/ --task grad --mode AB --model_name Fusion_AB --niter 0 --niter_decay 50 --batch_size 64 --reg_type none --init_type max --lr 0.002 --weight_decay 4e-4 --gpu_ids 0

```



## Reproducibility

To reporduce the results in our paper and for exact data preprocessing, implementation, and experimental details please follow the instructions here: [./data/TCGA_GBMLGG/](https://github.com/mahmoodlab/PathomicFusion/tree/master/data/TCGA_GBMLGG). Processed data and trained models can be downloaded [here](https://drive.google.com/drive/folders/1swiMrz84V3iuzk8x99vGIBd5FCVncOlf?usp=sharing).



## Issues

- Please open new threads or report issues directly (for urgent blockers) to [email protected].

- Immediate response to minor issues may not be available.



## Licenses, Usages, and Acknowledgements

- This project is licensed under the GNU GPLv3 License - see the [LICENSE.md](LICENSE.md) file for details. A provisional patent on this work has been filed by the Brigham and Women's Hospital.

- This code is inspired by [SALMON](https://github.com/huangzhii/SALMON) and [SCNN](https://github.com/CancerDataScience/SCNN). Code base structure was inspired by [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix).

- Subsidized computing resources for this project were provided by Nvidia and Google Cloud. 

- If you find our work useful in your research, please consider citing our paper at:



```bash

@article{chen2020pathomic,

  title={Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis},

  author={Chen, Richard J and Lu, Ming Y and Wang, Jingwen and Williamson, Drew FK and Rodig, Scott J and Lindeman, Neal I and Mahmood, Faisal},

  journal={IEEE Transactions on Medical Imaging},

  year={2020},

  publisher={IEEE}

}

```



© [Mahmood Lab](http://www.mahmoodlab.org) - This code is made available under the GPLv3 License and is available for non-commercial academic purposes.