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