Metadata-Version: 1.2
Name: deepsemhist
Version: 0.0.3
Summary: deep_semantic_histology: Deep Semantic Representations for Cancer Histology Images
Home-page: https://github.com/dakomura/deep_texture_histology
Author: Daisuke Komura
Author-email: kdais-prm@m.u-tokyo.ac.jp
Maintainer: Daisuke Komura
Maintainer-email: kdais-prm@m.u-tokyo.ac.jp
License: CC-BY-NC-SA 4.0
Download-URL: https://github.com/dakomura/deep_texture_histology
Description: ***************************************************************************************
        deep_semantic_histology : Tools for tissue/cell segmentation for histology images.
        ***************************************************************************************
        
        .. image:: https://github.com/dakomura/deep_semantic_histology/blob/main/docs/_static/logo/dsr_logo.jpg
        
        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``:
        
        .. code:: console
        
           $ 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 <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
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
