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, 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.
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
cv2
Recommended Environment
- OS
Linux (both CPU and GPU version)
Mac (both CPU and GPU version for M1 and M2 chip)
Windows (CPU version only)
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
See Also
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for deeptexture-0.3.4-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 01bb04052dd31481960428bc2d6561c2251449810b1863886d88c6a5a9a17068 |
|
MD5 | 0f64573fc7c88bd15992b936d5e6b9e6 |
|
BLAKE2b-256 | 3a6cbe5eea5ddb26769228fd68c11e894462927ae96d0a380103b1db9ae01a55 |