A software application built on top of openslide for viewing whole slide images (WSI) and performing pathological analysis
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}
}
- Responsive WSI image viewer
- State of the art cancer AI pipeline to segment and display the cancerous tissue regions
Running of the AI pipeline requires a GPU and several deep learning modules. However, you can run just the UI as well.
openslideflask
The following command will install only the dependencies listed above.
pip install DigiPathAI
pytorchtorchvisionopencv-pythonimgaugmatplotlibscikit-learnscikit-imagetensorflow-gpu >=1.14,<2pydensecrfpandaswget
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.
Traverse to the directory containing the openslide images and run the following command.
digipathai <host: localhost (default)> <port: 8080 (default)>
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)
- Avinash Kori (koriavinash1@gmail.com)
- Haran Rajkumar (haranrajkumar97@gmail.com)
