[2018 台灣人工智慧學校校友年會] 人工智慧與醫療應用 / 王淳恆
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1. The document discusses explainable medical AI and techniques like Grad-CAM and SHAP that can help explain AI models in medical contexts like diabetic retinopathy detection and gene expression prediction. 2. It describes using Grad-CAM to help explain a model for detecting diabetic retinopathy and using SHAP to explain a gene expression prediction model by determining the contribution of different histone modification signals. 3. The author advocates that explainable AI is very important for medical AI to help ensure models are not seen as "black boxes" and to build trust in the use of AI in healthcare.
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[2018 台灣人工智慧學校校友年會] 人工智慧與醫療應用 / 王淳恆
- 1. Explainable Medical AI 王淳恆, Andrew Wang 沐恩生醫 CTO
- 2. 2017/7
- 3. 2018/5
- 5. Why AI? Why Now? In Medical
- 6. Which picture is took by iPhone 6, HTC One or Samsung Galaxy S4?
- 7. Which picture is took by iPhone 6, HTC One or Samsung Galaxy S4? Accuracy about 97% In Kaggle
- 8. Camera Model Identification • The different Camera Model has different processing pipeline • The different processing pipeline has the different Noise
- 9. But, is AI Black Box?
- 10. Explainable AI
- 11. Explainable Medical AI • Grad-CAM for Diabetic Retinopathy Detection • SHAP for Gene Expression Prediction
- 12. Explainable Medical AI • Grad-CAM for Diabetic Retinopathy Detection • SHAP for Gene Expression Prediction
- 13. Diabetic Retinopathy Detection Ref: https://www.kaggle.com/c/diabetic-retinopathy-detection
- 15. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ref: https://arxiv.org/pdf/1610.02391.pdf Input Cat Dog
- 16. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ref: https://arxiv.org/pdf/1610.02391.pdf
- 17. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ref: https://arxiv.org/pdf/1610.02391.pdf
- 18. Input Grad-CAM Grad-CAM in Proliferative (增生 性) DR for Model Explanation No DR
- 19. Explainable Medical AI • Grad-CAM for Diabetic Retinopathy Detection • SHAP for Gene Expression Prediction
- 23. Epigenetics
- 24. Histone Modifications (HM) (組蛋白修飾) 組蛋白 染色體 染色質 核小體 雙股DNA 組蛋白 H3 是修飾最多的組蛋白 (此分析的對象) 此分析為組蛋白H3的 N-末端甲基化的修飾
- 25. HM Signal and Gene Expression (組蛋白修飾訊號與基因表達) High gene expression, label = 1 Low gene expression, label = 0 H3K4me1 H3K4me3 H3K9me3 H3K36me3 H3K27me3 組蛋白H3的N末端第4個賴胺酸(K)的甲基化(me) => Signals DNA的鹼基對(bp)
- 26. HM Signal and Gene Expression (組蛋白修飾訊號與基因表達) 轉錄起點 (前後50個鹼基對) H3K4me1 H3K4me3 H3K9me3 H3K36me3 H3K27me3 H3K4me3 H3K4me1 H3K36me3 H3K9me3 H3K27me3 -50bp +50bp
- 27. Histone Modifications Signal w/ Gene Expression Histone Modifications Signal w/o Gene Expression
- 30. DeepChrome SVC: SVM classification RFC: Random Forest Classifier
- 31. werDNANet • Modify 5x5 Filter to two layers 3x3 Filter (From VGGnet concept) • More layer and smaller filter (3x3) is better • Add Batch Normalization
- 32. Compare DeepChrome and werDNANet in Gene Expression Prediction Method AUC Score K Nearest Neighbor (Benchmark) 0.8654 DeepChrome 0.9257 werDNANet 0.9266 (Best)
- 33. werDNANet Andrew
- 34. Ensemble DeepChrome and werDNAnet with 5-fold #2
- 35. SHAP • SHapley Additive exPlanation • Shapley values: Determine how much each player in a collaborative game has contributed to its success.
- 36. SHAP of 4 and 9 in CNN 0 1 2 3 4 5 6 7 8 9
- 37. SHAP of 4 and 9 in CNN 0 1 2 3 4 5 6 7 8 9
- 38. SHAP of Gene Expression = 1 H3K9me3 Histone Modifications Signal: SHAP Value:
- 39. SHAP of Gene Expression = 0 Histone Modifications Signal: SHAP Value:
- 40. SHAP of Gene Expression = ? Histone Modifications Signal:
- 41. SHAP of Gene Expression = 0 Histone Modifications Signal: SHAP Value:
- 42. Summary • AI is not Black Box • Explainable AI is very important in Medial AI
- 43. 王淳恆 Andrew Wang • Muen Biomedical (沐恩生醫) • Expertise – Technical Manager in Mediatek – Medical AI (2018~) – Deep Learning in Image (2015~) – Finance Analysis using AI (2005~) – Algorithm and Chip Design in wireless communication and image processing (2001~)
- 44. 王淳恆 Andrew Wang • Kaggle競賽三面銀牌 (Kaggle: Google旗下資料科學與人工智慧競賽平台) • Kaggle史上最高獎金競賽銀牌
- 45. 王淳恆 Andrew Wang