Speeding Up the Deep Learning Development Life Cycle for Cancer Diagnostics

Speeding Up the Deep Learning Development Life Cycle for Cancer Diagnostics

EuroPython Conference via YouTube Direct link

Intro

1 of 28

1 of 28

Intro

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Speeding Up the Deep Learning Development Life Cycle for Cancer Diagnostics

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  1. 1 Intro
  2. 2 Our Mission
  3. 3 Cancer diagnostics today
  4. 4 Future cancer diagnosis not for everyone?
  5. 5 Cancer diagnostics tomorrow
  6. 6 About MindPeak
  7. 7 Our Team and Advisors
  8. 8 Example: cancer cell detection
  9. 9 Simplicity
  10. 10 Training a deep learning model
  11. 11 Goal: Test new ideas quickly
  12. 12 Overview: Idea stage
  13. 13 Idea Generation - without data
  14. 14 Data-driven idea generation
  15. 15 Efficient Annotations
  16. 16 Metrics - define your target goals
  17. 17 Metrics - Mindpeak example
  18. 18 Overview: Implementation stage
  19. 19 Code quality-comments as code
  20. 20 Code quality - use einops library
  21. 21 On reproducibility
  22. 22 Implementation stage - summary
  23. 23 Overview: Training & Evaluation stage
  24. 24 PyTorch Data Parallelization
  25. 25 Pytorch Distributed Data Parallelization
  26. 26 Dataset reduction techniques
  27. 27 Training + evaluation stage - summary
  28. 28 Disappointment

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