Deep Learning Identifies Morphological Features in Breast Cancer Predictive of Cancer ERBB2 Status and Trastuzumab Treatment Efficacy

📁 Sample data and models

Download link (~3.3Gb)

he-erbb2-github-data
 ├── tissue-samples
 │    ├── [ sample 1 ]
 │    ├──     ...
 │    └── [ sample n ]
 └── models
      └── Her2
           ├── fold-1.pth
           ├── fold-2.pth
           ├── fold-3.pth
           ├── fold-4.pth
           └── fold-5.pth
    

🔬 Inference

Attach to the docker container and run:

python inference.py -c configs/inference-config.json

The script generates a .csv file with predicted scores.

⚙️ Running docker image

• Clone the Repository
• CD to the docker folder cd docker
• Edit "DATA_VOLM" path in init-docker.sh - point to the data you downloaded through the linkd, e.g. /your/path/he-erbb2-github-data/. Other variables can remain default.
• Build an image: ./build-docker.sh
• Run a container: ./run-docker.sh
  • Containers run in an interactive mode
  • control + p, control + q will turn interactive mode into daemon mode, i.e. detach from a container without stopping it
  • Reattach container: docker attach [name]
  • Warning: control + a d – detach from TMUX session when inside a container. This will kill the container!

📃 Cite

Scientific Reports PDF
https://www.nature.com/articles/s41598-021-83102-6
DOI: https://doi.org/10.1038/s41598-021-83102-6

Bychkov, D., Linder, N., Tiulpin, A., Kücükel, H., Lundin, M., Nordling, S., Sihto, H., Isola, J., Lehtimäki, T., Kellokumpu‑Lehtinen, PL., von Smitten, K., Joensuu, H., Lundin, J. Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy. Sci Rep 11, 4037 (2021)

📝 License

This code is freely available only for research purposes. Commercial use is not allowed by any means.