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
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
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
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file DigiPathAI-0.1.5.tar.gz
.
File metadata
- Download URL: DigiPathAI-0.1.5.tar.gz
- Upload date:
- Size: 2.5 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.44.1 CPython/3.7.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 997ecc517f72772157770b863000d869d20a9375287b533738f4a8c7ed9c5878 |
|
MD5 | e05ca68b0da90f21ddac558845cd2d57 |
|
BLAKE2b-256 | 4988360733b5aaf3ff7d4156cc28ac3f578f065969db2ee55020471dd1f2bab6 |
File details
Details for the file DigiPathAI-0.1.5-py3-none-any.whl
.
File metadata
- Download URL: DigiPathAI-0.1.5-py3-none-any.whl
- Upload date:
- Size: 2.6 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.44.1 CPython/3.7.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6ba963c42a61028a773d4326c87949b542f87ee7c96c43802b4537c58a97b599 |
|
MD5 | f614c4c8a1d38a14f869f788a2861955 |
|
BLAKE2b-256 | 51dea3e8d4009994293704d35eb7fc99e64a4fb468273a90134d302d8f41303f |