deep_texture_histology: Deep Texture Representations for Cancer Histology Images
Project description
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 and content-based image retrieval are also implemented.
Installation
The package can be installed with pip:
$ pip install deeptexture
Prerequisites
Python version 3.6 or newer.
numpy >=1.20.3
tensorflow >=2.1.0
joblib >=0.13.2
Pillow >=8.0.1
nmslib >=2.0.6
matplotlib >= 3.5.0
scikit-learn >=1.1.0
seaborn >=0.10.1
pandas >=1.1.0
Recommended Environment
- OS
Linux
Mac
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
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