GSOC LibreHealth Automatic Labelling of radiology images

" Previously on LibreHealth GSOC 2020 "   [ Recap ] The demo backend was connected to the frontend and some of the apis were modified for easy connection. "Frontend completed"   [ Work ] Requirements for frontend completed so fas: 1) Login/SignUp 2) User can request images with bounding box annotations. 3) User can filter bounding box annotations based upon which model was used for annotations. 4) User can modify the bounding box location/disease/size in real time. Here are few screenshots: " Next week "   [ On LibreHealth ] 1) Complete the training of retinanet and integrate it to the tensorflow serving server. 2) Make modifications to the frontend if requested by the mentors :)

" Previously on LibreHealth GSOC 2020 "   [ Recap ] The demo of the app backend is completely dockerized and it is super easy to run now. "React js"   [ Work ] Now that the backend is ready it is time to work on the frontend. The frontend as of now me and my mentor decided will be in Reactjs. This week I made basic mods the react frontend added login page and sign up methods. Changed the backend to easily integrate with the React frontend. As I'm having placements I'm unable to work with my full dedication right now and I feel very bad about it. Anyways I will make sure I compensate everything by putting extra hours, all hours in the coming weeks. " Next week "   [ On LibreHealth ] 1. Complete the React app integration with the backend.

" Previously on LibreHealth GSOC 2020 "   [ Recap ] The demo of the app was ready but it was not dockerized. Running it would have been a nightmare. "Docker"   [ Work ] This week was all about learning about docker and make it easy to deploy my project. Which I successfully did thanks to docker compose. Docker compose is awesome if you want to build and run multiple container which communicate with each other. In our case as of now we have 3 docker containers communicating with each other. " Next week "   [ On LibreHealth ] 1. Add support for the react app.

" Previously on LibreHealth GSOC 2020 "   [ Recap ] The Django Backend and TF-Serving has been successfully implemented. The communication between the two is facilitated by Python based middleware. " Synchronous to Asynchronous "   [ Work ] This week was all about converting the python middleware into asynchronous service and generating Doc strings and documentation as the end of first month is nearing. Fortunately, I have completed my commitments for the first month and right no, I'm polishing the code. Python's Asyncio module was used to get the async await features. For facilitating dataset storage, I have looked into NextCloud. " Next week "   [ On LibreHealth ] 1) Train the NIH Xray dataset on a different object detection algo and add a support for selecting different ML algos. 2) Complete all documentations.

" Previously on LibreHealth GSOC 2020 "   [ Recap ] The Django server is implemented successfully. The Django backend is a Restful server that is responsible for communication between user and TF-Serving. Also, it is responsible for storing user data. "T ensorflow Serving "   [ Work ] The diagram above explains how we plan to deploy our ML models. Basically, the python middleware sends images to the TF Serving and receives BBox information and then send the received data to client. The client in this case is the Django Rest Server. The Django server stores Images and BBox data in a SQL DB. The User can now request for image and Bbox data from Django Server. As of now everything is implemented, but improvements will be made to the python based Middleware. " Next week "   [ On LibreHealth ] 1) Make the Python middleware Asynchronous and document everything. 2) Decide what type of file storage server we want to implement.

" Previously on LibreHealth GSOC 2020 "   [ Recap ] The community bonding period has ended and we are clear with our requirements of what we are going to be doing for the summer. " Django backend "   [ Work ] The server in the above figure is a combination of 2 servers the django-server and Tensorflow servering. Basically, the Django server acts as the middleman between the Client and tensorflow serving. I have implemented the API for Django-server. I have used generic views and model serializers. The DB is sqlLite. " Next week "   [ On LibreHealth ] 1) Training the tf model. 2) Setup tf-serving container.

" Previously on LibreHealth GSOC 2020 "   [ Recap ] There was change of plans, and the project's audience and essence was changed, for the better. Now the project has the potential to become something amazing. A new architecture was proposed to complement the new plan. And work on the UI was started. " The count down begins "   [ Final checks ] The coding is about to start and I'm ready. I am read up and very excited. The core client feature of bounding Box labelling is ready. And working like a charm. The frontend looks like this I know it is very basic but it will be improved, no need to worry. :) So, before the coding session starts we will be having a nice bug free UI to work with. Now I'm going to be reading all the articles I can find on tensorflow-serving. I wanna make this so robust and fast that it feels like a hot knife on butter. Even in React I have used only 1 class component, rest are functional. The UI needs to be polished and wicked fast.