GSOC LibreHealth Automatic Labelling of radiology images

" 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.

" Previously on LibreHealth GSOC 2020 "   [ Recap ] After forking the repository and writing the Backend, it looked like every thing was going fine the papers were awesome and everything was going well. " The new Changes"     [ Change of plans ] The new project in its essence in is very different but technology and implementation wise it is not that big of a change. See I thought we were building a webapp for people to diagnose diseases. Well, that is not at all true. We are building a webapp that annotates diseases with bounding boxes to assist doctors during annotation period. To be a helping hand. " The new Architecture"      [ Scale ] The architecture will remain largely the same. But we are taking into account in a real world scenario multiple people will be doing annotations job so, it would be waste if we did inference for each doctor when they for images. We can speed it up. Here is what we will do. " The frontend"      [ BBoxes ] The fr

" Previously on LibreHealth GSOC 2020 "   [ Recap ] Kislay had celebrated his selection, told all about it to the friends and family, before he embarked on this journey, like a good traveller he has packed up, while in this context it was updating drivers, installing ubuntu and saying good bye to windows for some time. It was then he understood the quote " I get ideas about what's essential when I'm packing my suitcase" by Diane von Furstenberg. Now: " Django "   [ Gitlab ] I have created a new django app and setup a free smtp server from send in blue. The django app is capable of authenticating users by sending them email. I have committed all the changes to the forked repo. Since the plan is to use Reactjs frontend so I have used Django-rest-framework. For authentication I have used rest-auth. "awesome library" The steps to setup your own rest authentication service using Django: Install django rest auth and allauth: pip install django

" Automatic labeling of radiology images " [ Project ] I will be developing a stand alone web app. The web app will be able to segment, annotate and classify the uploaded X-ray through different deep learning algorithms such as GradCam and yoloV3. Now the architecture that I have proposed is a server client Restful architecture. The backend will be done in Django and Tensorflow serving( the machine learning framework of choice is Tensorflow2.1 ) and the frontend in Reactjs. The final goal is to make a beautiful web app that is scalable, responsive and beautiful. Here is a diagram of the architecture that we will be building: Project   [ Project architecture ] "Community bonding week 1" [ Work ] As first weeks at any organisation goes, I read up the forums and tried to get insights on the community. I also set up my development environment. I updated my visual studio code and cuda toolkit to 10.2. Enjoyed the fact that I got selected for GSOC 2020. "What can b