deep_texture_histology : Tools for deep texture representation for histology images.

Overview

deep_texture_representation is a python library to calculate deep texture representations (DTRs) for histology images (Cell Reports, 2022). Fucntions for plotting the distribution of DTRs, content-based image retrieval, and supervised learning are also implemented.

Installation

The package can be installed with pip:

$ pip install deeptexture

Conda environmental files including dependent libraries for various OS are available here.

To test the successful installation,

$ git clone https://github.com/dakomura/deep_texture_histology
$ cd deep_texture_histology
$ python check_libraries_and_quick_test.py

Prerequisites

Python version 3.6 or newer.

• numpy
• tensorflow
• joblib
• Pillow
• nmslib
• matplotlib
• scikit-learn
• seaborn
• pandas
• cv2

All the required libraries can be installed with conda yml files. See https://github.com/dakomura/dtr_env

Recommended Environment

• OS

  • Linux (both CPU and GPU version)
  • Mac (both CPU and GPU version for M1 and M2 chip)
  • Windows (both CPU and GPU version)

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC-BY-NC-SA 4.0)

For non-commercial use, please use the code under CC-BY-NC-SA.

If you would like to use the code for commercial purposes, please contact us <ishum-prm@m.u-tokyo.ac.jp>.

Citation

If you use this library for your research, please cite:

Komura, D., Kawabe, A., Fukuta, K., Sano, K., Umezaki, T., Koda, H., Suzuki, R., Tominaga, K., Ochi, M., Konishi, H., Masakado, F., Saito, N., Sato, Y., Onoyama, T., Nishida, S., Furuya, G., Katoh, H., Yamashita, H., Kakimi, K., Seto, Y., Ushiku, T., Fukayama, M., Ishikawa, S.,

"Universal encoding of pan-cancer histology by deep texture representations."

Cell Reports 38, 110424,2022. https://doi.org/10.1016/j.celrep.2022.110424

Documentation

Documentation