Weakly-Supervised, Large-Scale Computational Pathology for Diagnosis and Prognosis - Max Lu

Weakly-Supervised, Large-Scale Computational Pathology for Diagnosis and Prognosis - Max Lu

Stanford MedAI via YouTube Direct link

Primary origins of ceps

23 of 37

23 of 37

Primary origins of ceps

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Weakly-Supervised, Large-Scale Computational Pathology for Diagnosis and Prognosis - Max Lu

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  1. 1 Introduction
  2. 2 Welcome
  3. 3 Background
  4. 4 Example
  5. 5 General workflow
  6. 6 Can we train accurate diagnostic or problem prognostic models
  7. 7 The same label assumption
  8. 8 Multiple instance learning
  9. 9 Data efficiency
  10. 10 Recap
  11. 11 Framework
  12. 12 Segmentation
  13. 13 Embedding
  14. 14 Attention pooling
  15. 15 Summary
  16. 16 Benchmarks
  17. 17 Attention scores
  18. 18 Cell phone microscopy
  19. 19 Results
  20. 20 Summarize
  21. 21 Code
  22. 22 Prognosis
  23. 23 Primary origins of ceps
  24. 24 Study design
  25. 25 Classification
  26. 26 Heatmaps
  27. 27 Interactive demo
  28. 28 Attention heating map
  29. 29 Dummy tool
  30. 30 High certainty diagnosis
  31. 31 Differential diagnosis
  32. 32 Thank you
  33. 33 Which regions in the slide will contribute
  34. 34 Can the primary originate from one single primary
  35. 35 Is the morphology more nuanced
  36. 36 Clustering
  37. 37 Outro

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