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digipathai

Tool to visualize gigantic pathology images and use AI to segment cancer cells and present as an overlay

https://github.com/haranrk/digipathai

Science Score: 23.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: arxiv.org, ncbi.nlm.nih.gov
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (16.8%) to scientific vocabulary

Keywords

cancer-research deep-learning gui medical-image-analysis openslide python segmentation wsi
Last synced: 6 months ago · JSON representation

Repository

Tool to visualize gigantic pathology images and use AI to segment cancer cells and present as an overlay

Basic Info
  • Host: GitHub
  • Owner: haranrk
  • License: mit
  • Language: JavaScript
  • Default Branch: master
  • Homepage:
  • Size: 138 MB
Statistics
  • Stars: 73
  • Watchers: 5
  • Forks: 27
  • Open Issues: 9
  • Releases: 1
Topics
cancer-research deep-learning gui medical-image-analysis openslide python segmentation wsi
Created over 6 years ago · Last pushed about 1 year ago
Metadata Files
Readme Funding License

README.md

PyPI version PyPI Downloads arXiv

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

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(imgpath, patchsize = 256, stridesize = 128, batchsize = 32, quick = True, ttalist = None, crf = False, savepath = None, status = None) ```

Contact

  • Avinash Kori (koriavinash1@gmail.com)
  • Haran Rajkumar (haranrajkumar97@gmail.com)

ko-fi

Owner

  • Name: Haran Rajkumar
  • Login: haranrk
  • Kind: user
  • Location: Coimbatore
  • Company: Money Forward

Software Engineer at Money Forward

GitHub Events

Total
  • Watch event: 8
  • Push event: 1
  • Fork event: 2
Last Year
  • Watch event: 8
  • Push event: 1
  • Fork event: 2

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 125
  • Total Committers: 4
  • Avg Commits per committer: 31.25
  • Development Distribution Score (DDS): 0.432
Past Year
  • Commits: 1
  • Committers: 1
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
haranrk h****7@g****m 71
koriavinash1 k****1@g****m 50
mahendrakhened m****d@g****m 3
The Codacy Badger b****r@c****m 1
Committer Domains (Top 20 + Academic)
codacy.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 15
  • Total pull requests: 1
  • Average time to close issues: 20 days
  • Average time to close pull requests: less than a minute
  • Total issue authors: 9
  • Total pull request authors: 1
  • Average comments per issue: 0.73
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • koriavinash1 (4)
  • haranrk (4)
  • Arghya999 (1)
  • juliandwillett (1)
  • mahendrakhened (1)
  • bushra-tayyaba (1)
  • jemjemster (1)
  • bbkaran (1)
  • codeskings (1)
Pull Request Authors
  • codacy-badger (1)
Top Labels
Issue Labels
immediate (3) enhancement (2)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 36 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 13
  • Total maintainers: 2
pypi.org: digipathai

Deep Learning toolbox for WSI (digital histopatology) analysis

  • Versions: 13
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 36 Last month
Rankings
Forks count: 7.8%
Stargazers count: 8.7%
Dependent packages count: 10.0%
Average: 14.2%
Dependent repos count: 21.7%
Downloads: 22.7%
Maintainers (2)
avin.kori haranrk
Last synced: 6 months ago

Dependencies

setup.py pypi
  • flask *
  • openslide-python *