/LCT-MVP

Web platform for annotating medical images and generating pathologies on studies of healthy patients. MVP of the FutureOfMedTech team, developed for the LCT 2022 hackathon.

Primary LanguageJavaScriptMIT LicenseMIT

𝕃ℂ𝕋-𝕄𝕍ℙ

Web platform for annotating medical images and generating pathologies on studies of healthy patients. MVP of the FutureOfMedTech team, developed for the LCT 2022 hackathon.

Screenshots

Screenshot 2023-04-12 at 00 40 19

Screenshot 2023-04-12 at 00 48 01

Screenshot 2023-04-12 at 00 50 38

Screenshot 2023-04-12 at 00 44 18

Start

Clone

git clone https://github.com/GVatest/LCT-MVP
cd hedge-fund

Backend

Install

pip install -r requirements.txt

Start

Create database

python manage.py migrate

Start server

python manage.py runserver

Frontend

Install

npm install
# or
yarn install

Start

npm run dev
# or
yarn dev

About

  • API was developed based on django rest framework using sqlite3 database, as it is an mvp project, working with sqlite3 was easier and faster than its more advanced alternatives such as postgresql.
  • Authorization is implemented based on JWT tokens
  • The frontend is implemented using the React library in conjunction with the Redux state manager
  • Loading and processing of Dicom slices on the front end is done using the open-source library [DWV] (https://www.npmjs.com/package/dwv)
  • The functionality of the annotator is based on the functionality of the library for working with computer vision algorithms [OpenCV.js] (https://docs.opencv.org/4.6.0/d5/d10/tutorial_js_root.html)

Stack

Frontend

Backend

Additional

I was the member of FutureOfMedTech team at the 2022 hackathon. As a full stack developer I took responsibility for writing the API, designing the database, and writing individual front-end modules: set up JWT authentication, loading, primary processing and displaying Dicom slides, participated in writing annotator functionality: annotation tools, import and export of annotation to Json, multiple image loading, image scrolling. Also took responsibility for merge of three application modules: pathology generation module, Dicom image annotator, studies and staff management system. The final challenge was to deploy and configure the application on a remote server, for this I used nginx + gunicorn.

Credits

Contributors:

License

Licence