https://github.com/smaranjitghose/auto_vaidya

An open-source project for applying deep learning to medical scenarios
https://github.com/smaranjitghose/auto_vaidya

ch-20 deeplearning hacktoberfest medical-imaging python slop slop20 streamlit tensorflow

Last synced: about 1 month ago
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An open-source project for applying deep learning to medical scenarios

• Host: GitHub
• URL: https://github.com/smaranjitghose/auto_vaidya
• Owner: smaranjitghose
• License: cc0-1.0
• Created: 2020-06-05T21:20:31.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-11-22T10:14:21.000Z (about 2 years ago)
• Last Synced: 2023-03-07T21:04:15.003Z (almost 2 years ago)
• Topics: ch-20, deeplearning, hacktoberfest, medical-imaging, python, slop, slop20, streamlit, tensorflow
• Language: Python
• Homepage:
• Size: 101 MB
• Stars: 18
• Watchers: 3
• Forks: 19
• Open Issues: 11
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
Auto Vaidya


An open source solution for creating end-end web app for employing the power of deep learning in various clinical scenarios like implant detection, pneumonia detection, brain mri segmentation etc.

## Suggestions for PR:

- Please give your PR for the test branch unless requested otherwise by the project maintainer

- Name your PR appropiately

- Ensure that you had already raised an issue for this PR and the project maintainer had approved and assigned you

- In the PR description, typically the following are expected:

    - Dataset Used:

    - Dataset Size:

    - Dataset Source:

    - Link to Colab Notebook: Please ensure you give access for view to anyone with link

    - Your Exploratory Data Analysis [Snapshots of the relevant ones and your inference from that]

    - Any Pre-Processing methods used. [Elaborate on them]

    - Your framework to train

    - Different methods used for training

    - Test/Train Split

    - Results: Please do not simply state test accuracy. Other perfomance metrics like F1 score,etc are expected

    - ** Draw a table to show the comparitive analysis of the performance of the different methods you used

    - Conclusion: Which method you think is best and why?

- A copy of the notebook used for your training is expected inside the ``notebooks/`` directory.

- Please name the notebook as ```name_of_the_problem_your_github_username```

- The model files are expected to be inside a ```models\name_of_your_problem\``` directory

- If you are using TensorFlow 2.0, please give both the h5 as well as saved_model file

- Once your PR, gets approved uptil this, proceed with a follow up pr to integrate it inside the streamlit app. Refer [this](https://github.com/smaranjitghose/img_ai_app_boilerplate) if you are unaware of how to use streamlit and host it

- For the streamlit app, it would be a good practice if you define the function for classification/prediction/regression inside a separate python file say ```your_problem_name.py``` and import it inside ```app.py``` ( Believe me this would save a lot of time otherwise wasted in debugging)

- For the second PR, you are expected to do the above changes and provide screenshots/a small clip of the working model of the app after integrating your model from the previous PR

- For the second PR, it should be one the test branch only, later the project maintainers will merge it with the master branch for a stable release

- For PRs, related to frontend please give it to the ```frontend``` branch

- Once accepted, give a follow up PR to the ```test``` branch to render your html,css files for a page using streamlit

- As stated above you are expected to give screenshots, descriptions and other details for the PR

Entire App on Heroku: https://auto-vaidya.herokuapp.com/

Frontend on  Netlify: autovaidya.netlify.app