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# 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 

### Citation

If you find this reference implementation useful in your research, please consider citing:

```

@article{khened2020generalized,

  title={A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis},

  author={Khened, Mahendra and Kori, Avinash and Rajkumar, Haran and Srinivasan, Balaji and Krishnamurthi, Ganapathy},

  journal={arXiv preprint arXiv:2001.00258},

  year={2020}

}

```

# Features

- Responsive WSI image viewer 

- State of the art cancer AI pipeline to segment and display the cancerous tissue regions

# 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>

# 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)

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