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PathOmics: Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction

The official code of "Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction" (Accepted to MICCAI2023, top 9%).

<b> Our Paper </b> [Link]

<b> [New Update!!!] We updated the paper list of pathology-and-genomics multimodal analysis approaches in healthcare at the end of this repo. </b>

Workflow overview of the PathOmics

<b> Workflow overview of the pathology-and-genomics multimodal transformer (PathOmics) for survival prediction. </b> In (a), we show the pipeline of extracting image and genomics feature embedding via an unsupervised pretraining towards multimodal data fusion. In (b) and (c), our supervised finetuning scheme could flexibly handle multiple types of data for prognostic prediction. With the multimodal pretrained model backbones, both multi- or single-modal data can be applicable for our model fine-tuning.

Citation

@inproceedings{ding2023pathology,
  title={Pathology-and-genomics multimodal transformer for survival outcome prediction},
  author={Ding, Kexin and Zhou, Mu and Metaxas, Dimitris N and Zhang, Shaoting},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={622--631},
  year={2023},
  organization={Springer}
}

Prerequisites

python 3.8.18
Pytorch 2.0.1
pytorch-cuda 11.8
Torchvision 0.15.2
Pillow 9.4.0
numpy 1.24.3
pandas 2.0.3
scikit-survival 0.21.0 
scikit-learn 1.2.0
h5py 2.8.0

Usage

Data prerpocessing

1. Download WSIs from TCGA-COAD and TCGA-READ.
2. Download genomics data from CbioPortal and move the downloaded folder into "PathOmics" folder.
* "coadread_tcga_pan_can_atlas_2018" in `bash_main.py` and `bash_main_read.py` is the downloaded folder, please download it before you run the code.
3. Split WSIs into patches and only keep the foreground patches.
4. Extract patch features via pretrained models (e.g., ImageNet-pretrained ResNet50, ResNet101, etc).
5. Save patch features as .npz files. (For each slide, we generate one .npz file to save patch features).

For more details about extracting feature, please check Issue 1 and the code in split_tiles_utils/helper.py

Run code on TCGA-COAD only

Model will be pretrained and finetuned on theTCGA-COAD training set (4-fold cross-validation). The finetuned model will be evaluated on the TCGA-COAD hold-out set.

python bash_main.py --pretrain_loss 'MSE' --save_model_folder_name 'reproduce_experiments' --experiment_folder_name 'COAD_reproduce' --omic_modal 'miRNA' --kfold_split_seed 42 --pretrain_epochs 25 --finetune_epochs 25 --model_type 'PathOmics' --model_fusion_type 'concat' --model_pretrain_fusion_type 'concat' --cuda_device '2' --experiment_id '1' --use_GAP_in_pretrain_flag --seperate_test

Run code on TCGA-COAD and TCGA-READ

Model will be pretrained on TCGA-COAD (5-fold cross-validation). Model will be finetuned, validated, and evaluated on the TCGA-READ dataset.

python bash_main_read.py --k_fold 5 --fusion_mode 'concat' --prev_fusion_mode 'concat' --pretrain_loss 'MSE' --save_model_folder_name 'reproduce_experiments' --experiment_folder_name 'READ_reproduce' --omic_modal 'miRNA' --kfold_split_seed 42 --pretrain_epochs 25 --finetune_epochs 25 --model_type 'PathOmics' --cuda_device '2' --experiment_id '1' --use_GAP_in_pretrain_flag

If you want to use TCGA-COAD pretrain weights and skip the pretraining stage, please add --load_model_finetune into your script. Please modify the code to ensure your pretrain weights saving directory is correct.

Use data-efficient mode in finetuning stage

Please add --less_data into your script and set --finetune_test_ratio as your preferred ratio for indicating the ratio of data used for model finetuning.

Literature reviews of pathology-and-genomics multimodal analysis approaches in healthcare.