Prize Categories Enrolled for:
(Please tick any specific Category that applies)
[ ] the Best use of Microsoft Azure
[x] 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
Before you post the issue:
[x] You have followed the issue title format.
[x] You have mentioned the correct labels.
[x] You have provided all the information correctly.
Before you start, please follow this format for your issue title:
npHard - DL-Vizard
ℹ️ Project information
Please complete all applicable.
🔥 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
Before you post the issue: