deep_texture: Deep Texture Representations for Cancer Histology Images

Notice: We have modified the code to create a deep texture library that can be installed with pip.
See below for details.

pip: https://pypi.org/project/deeptexture/

document: https://deep-texture-histology.readthedocs.io/en/latest/index.html

github: https://github.com/dakomura/deep_texture_histology

The old version below is no longer supported.

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).

Code Description

# Installation

conda create -n deep_texture python=3.6
source activate deep_texture

conda install -c anaconda cudatoolkit==9.0
conda install -c anaconda cudnn==7.6.5

pip install pillow
pip install tensorflow-gpu==1.10.0
pip install keras==2.2.3

pip install git+https://github.com/keras-team/keras-applications.git@d506dc82d0

## usage
import deep_texture

(prep, dnn) = deep_texture.setup_texture(arch = 'nasnet', layer = 'normal_concat_11', cbp_dir = '/tmp')

dtr = deep_texture.calc_features_file("./test.png", prep, dnn)

Citation

If you use this code for your research, please cite our paper.

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., 2022. Universal encoding of pan-cancer histology by deep texture representations. Cell Reports 38, 110424. https://doi.org/10.1016/j.celrep.2022.110424