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Deep Learning toolbox for WSI (digital histopatology) analysis

Project description


A software application built on top of openslide for viewing whole slide images (WSI) and performing pathological analysis


  • Responsive WSI image viewer
  • State of the art cancer AI pipeline to segment the and display the cancer cell

Application Overview


Online Demo


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


  • openslide
  • flask

The following command will install only the dependencies listed above.

pip install DigiPathAI

Entire AI pipeline


  • 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.


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)


Project details

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Files for DigiPathAI, version 0.1.3
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