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Applying deep learning to medical data

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의료 AI를 잠시 구경한 초심자의 경험담.

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Applying deep learning to medical data

  1. 1. Applying Deep Learning to Medical Data - AI 초심자의 의료 AI 입문기 민 현 석 올해 초까진 삼성전자 AI Lab 연구원 잠시 육아백수이지만, 경험을 공유하러 온 의료 AI 초심자 --
  2. 2. 저는 최근에…
  3. 3. 저는 최근에…
  4. 4. 의료 AI를 시작하기 전 제가 꿈꾸던 모습…
  5. 5. 의료 AI를 시작하기 전 제가 알던 모습…
  6. 6. 의료 AI를 시작하기 전 제가 알던 모습… [1] Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features, http://www.nature.com/articles/ncomms12474 [2] SURVIVALNET: PREDICTING PATIENT SURVIVAL FROM DIFFUSION WEIGHTED MAGNETIC RESONANCE IMAGES USING CASCADED FULLY CONVOLUTIONAL AND 3D CONVOLUTIONAL NEURAL NETWORKS, https://arxiv.org/pdf/1702.05941v1.pdf
  7. 7. Deep learning is Data-Driven approach MORE DATA
  8. 8. Deep learning is Data-Driven approach MORE DATA [1] Revisiting Unreasonable Effectiveness of Data in Deep Learning Era, https://arxiv.org/pdf/1707.02968v1.pdf
  9. 9. Deep learning is Data-Driven approach MORE DATA REAL WORLD Training data
  10. 10. Data is problem !! MORE DATA, GOOD DATA REAL WORLD Training data
  11. 11. Data is problem !! We have data!!
  12. 12. 왜 어려운가? 실습 데이터 vs. 의료 데이터: 풍요속의 빈곤, 의료 데 이터
  13. 13. Unclear annotations https://camelyon16.grand-challenge.org/data/
  14. 14. Unbalanced data
  15. 15. Medical image != toy example
  16. 16. Medical image != toy example
  17. 17. Difference between domains —
  18. 18. 의료 데이터 문제 이유? 1. 의사는 학습 데이터 만드는 사람이 아니다! 2. 의료 데이터는 바둑 기보가 아니다! 3. 병원은 환자의 데이터가 있는 곳이다! 4. 의료 데이터는 일반 데이터와는 그 성질이 다르다!
  19. 19. 데이터 문제 해결을 위한 시도들? AI with Data vs. AI for Data
  20. 20. Data, more data Deep Learning is Robust to Massive Label Noise, https://arxiv.org/pdf/1705.10694v2.pdf
  21. 21. Data, more data Deep Learning is Robust to Massive Label Noise, https://arxiv.org/pdf/1705.10694v2.pdf
  22. 22. Pre-trained Model with adequate fine-tuning Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?, https://arxiv.org/abs/1706.00712 e use of the fine-tuning approach > full training from scratch
  23. 23. Pre-trained Model with adequate fine-tuning Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?, https://arxiv.org/abs/1706.00712
  24. 24. Patch based approach
  25. 25. Different Network architecture High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks, https://arxiv.org/abs/1703.07047
  26. 26. Uncertainty What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? (https://arxiv.org/pdf/1703.04977.pdf)
  27. 27. Uncertainty Leveraging uncertainty information fromdeep neural networks for disease, detectionhttp://biorxiv.org/content/biorxiv/early/2016/10/28/084210.full.pdf
  28. 28. Data selection using Uncertainty Leveraging uncertainty information fromdeep neural networks for disease, detectionhttp://biorxiv.org/content/biorxiv/early/2016/10/28/084210.full.pdf
  29. 29. Reverse Classification Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth (http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7902121), IEEE TRANSACTIONS ON MEDICAL IMAGING, 2017)
  30. 30. Medical Image Synthesis Medical Image Synthesis with Context-Aware Generative Adversarial Networks (https://arxiv.org/pdf/1612.05362v1.pdf)
  31. 31. Domain adaptation (Stain color normalization with deep learning) —
  32. 32. Domain adaptation (Stain color normalization with deep learning) —
  33. 33. Nevertheless
  34. 34. Publish Houses of Brick, not mansions of straw https://www.nature.com/news/publish-houses-of-brick-not-mansions-of-straw-1.22029 The main question when reviewing a paper should be whether its conclusions are likely to be correct, not whether it would be important if it were true. Real advances are built with bricks, not straw.
  35. 35. 의료 데이터 문제 해결을 위해 1. 일반 AI에서 쓰인 문제 해결 전략도 의료 데이터에 쓰일 수 있음! 2. 의료 데이터에 맞게 변형된 전략이 사용될 때 좋은 성능 보임! 그러나 많은 전략들은 ‘문제의 해결’이 아닌 ‘문제의 완화’ 의료 AI는 문제의 해결을 위해 계속 노력해야 하는 분야
  36. 36. 한국 의료 AI는 어떻게 문제를 해결해야 하는가? AI or 의료 vs. 의료 with AI: 최고의 스승이자 고객
  37. 37. If AI kids do not know medical…. Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results , https://arxiv.org/pdf/1707.09585.pdf
  38. 38. Pictures Are Not Taken in a Vacuum ctures are not taken in a vacuum – An overview of exploiting context for semantic scene content understanding, IEEE Signal Processing Magazine ·April 2006
  39. 39. Medical image + context data Prediction of Kidney Function from Biopsy Images Using Convolutional Neural Networks, https://arxiv.org/pdf/1702.01816v1.pdf Training with initial eGFR: Decreasing the training time by 2x Decreasing the validation error by 20%
  40. 40. Medical image + context data A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data (https://arxiv.org/pdf/1705.08111v2.pdf), MICCAI 2017
  41. 41. Medical image + Medical image Brain Tumor Segmentation with Deep Neural Networks, https://arxiv.org/abs/1505.03540
  42. 42. The efficient way to use context data ? Real-Time User-Guided Image Colorization with Learned Deep Priors (https://arxiv.org/abs/1705.02999, SIGGRAPH 2017)
  43. 43. The efficient way to use context data ? What Can Help Pedestrian Detection? (CVPR 2017, https://arxiv.org/pdf/1705.02757v1.pdf)
  44. 44. The efficient way to use context data ? Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction, https://arxiv.org/pdf/1511.05756.pdf
  45. 45. The efficient way to use context data ? A simple neural network module for relational reasoning, https://arxiv.org/pdf/1706.01427.pdf
  46. 46. 의료 데이터는 환자의 데이터!! 1. AI 전문가만으로의 연구는 한계가 분명! 2. 기존 의학 지식/수치등은 AI의 좋은 스승이자 데이터! 3. Deep learning은 이질적인 데이터를 융합하기에 적합! 의료 AI는 ‘AI 전문가가 데이터만 있으면 해결할 수 있는 문제’가 아니 라, ‘의료 전문가와 AI 전문가가 경험과 데이터를 가지고 함께 풀어야 하는 문제’!!
  47. 47. 별거 아닌 내용의 세미나였지만, 제 시간을 마무리 하기 전에…
  48. 48. 감사합니다.

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