Metadata-Version: 2.1
Name: DigiPathAI
Version: 0.1.2
Summary: Deep Learning toolbox for WSI (digital histopatology) analysis
Home-page: https://github.com/haranrk/DigiPathAI
Author: Avinash Kori, Haran Rajkumar
Author-email: koriavinash1@gmail.com, haranrajkumar97@gmail.com
License: UNKNOWN
Description: # DigiPathAI
        A software application built on top of [openslide](https://openslide.org/) for viewing [whole slide images (WSI)](https://www.ncbi.nlm.nih.gov/pubmed/30307746) and performing pathological analysis 
        
        # Features
        - Responsive WSI image viewer 
        - State of the art cancer AI pipeline to segment the and display the cancer cell
        
        # Application Overview
        <p align="center">
          <img src="imgs/demo.gif">
        </p>
        
        # Results
        <p align="center">
          <img width="460" height="300" src="imgs/results_1.png">
        </p>
        
        # Online Demo
        https://digipathai.tech/
        
        # Installation
        Running of the AI pipeline requires a GPU and several deep learning modules. However, you can run just the UI as well.
        
        ## Just the UI
        ### Requirements
        - `openslide`
        - `flask`
        
        The following command will install only the dependencies listed above.
        ```
        pip install DigiPathAI
        ```
        
        ## Entire AI pipeline
        ### Requirements
        - `pytorch`
        - `torchvision`
        - `opencv-python`
        - `imgaug`
        - `matplotlib`
        - `scikit-learn`
        - `scikit-image`
        - `tensorflow-gpu >=1.14,<2`
        - `pydensecrf`
        - `pandas`
        - `wget`
        
        The following command will install the dependencies mentioned
        ```
        pip install "DigiPathAI[gpu]"
        ```
        
        Both installation methods install the same package, just different dependencies. Even if you had installed using the earlier command, you can install the rest of the dependencies manually. 
        
        # Usage 
        ## Local server
        Traverse to the directory containing the openslide images and run the following command.
        ```
        digipathai <host: localhost (default)> <port: 8080 (default)>
        ```
        
        ## Python API usage
        The application also has an API which can be used within python to perform the segmentation. 
        ```
        from DigiPathAI.Segmentation import getSegmentation
        
        prediction = getSegmentation(img_path, 
        			patch_size  = 256, 
        			stride_size = 128,
        			batch_size  = 32,
        			quick       = True,
        			tta_list    = None,
        			crf         = False,
        			save_path   = None,
        			status      = None)
        ```
        
        # Contact
        - Avinash Kori (koriavinash1@gmail.com)
        - Haran Rajkumar (haranrajkumar97@gmail.com)
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.5
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Provides-Extra: gpu
