deepsemhist 0.0.3

deep_semantic_histology: Deep Semantic Representations for Cancer Histology Images

Overview

deep_semantic_representation is a python library to apply tissue/cell segmentation models for histology images (bioRxiv, 2022). Fucntions for plotting the distribution are also implemented.

Installation

The package can be installed with pip:

$ pip install deepsemantic

Prerequisites

Python version 3.6 or newer.

• numpy >=1.20.3

• 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

  • 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 <[e-mail]>.

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