Deep Learning toolbox for WSI (digital histopatology) analysis
Project description
DigiPathAI
A software application built on top of openslide for viewing whole slide images (WSI) 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
Results
Online Demo
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)
Project details
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