TEAM NAME - PROJECT NAME

[arjunmohnot]

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npHard - DL-Vizard

ℹ️ Project information

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• Project Name: DL-Vizard
• Description: Visualisation tool for keras
• Team Name: npHard
• Team Members: Arjun Mohnot(@arjun009) / Kumar Utkarsh(@Utkarsh07) / Vipul Gupta(@vipul9810) / Gagan Talreja(@gagantalreja)
• Where to find you?: - LT-19
• Demo Link: https://drive.google.com/open?id=1Gkx2kcAP1siZ75xKTFzfr5tAKfdNOOdW
• Repository Link(s): https://github.com/Arjun009/LNMHacks4-Submissions/tree/master/npHard
• Prize Categories Enrolled for:
  (Please tick any specific Category that applies)
  • the Best use of Microsoft Azure
  • Best Beginner (Your team should be at least 50% Beginners)
  • Best "Jugaad" - Best Fun/ Silly/ Innovative Hack
  • Best UIPath Hack

🔥 Your Pitch

Kindly write a pitch for your project. Please do not use more than 500 words

One of the most debated topics in deep learning is how to interpret and understand a trained model – particularly in the context of high-risk industries like healthcare. The term “black box” has often been associated with deep learning algorithms. How can we trust the results of a model if we can’t explain how it works? Take the example of a deep learning model trained for detecting cancerous tumours. The model tells you that it is 99% sure that it has detected cancer – but it does not tell you why or how it made that decision. Did it find an important clue in the MRI scan? Or was it just a smudge on the scan that was incorrectly detected as a tumour? This is a matter of life and death for the patient and doctors cannot afford to be wrong.

🔦 Any other specific thing you want to highlight?

(Optional)
Love 😍 to see more such hacks!

✅ Checklist

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