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Bringing Interpretable Models to Cancer Precision Medicine
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Classroom Contents
Integrating AI and Machine Learning in Life Sciences - Allen School Colloquia
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- 1 Intro
- 2 Al for Bio-Medical Sciences (AIMS) Lab
- 3 Overview
- 4 Published Al models detect COVID-19 in chest X-rays
- 5 What was the training data for these published models?
- 6 How can we test how robust the models are?
- 7 How robust are the models?
- 8 What is important for the model's predictions?
- 9 Can we fix shortcut learning with improved data?
- 10 Conclusions
- 11 Acknowledgements
- 12 Outline
- 13 Explainable AI
- 14 Examples
- 15 Intuition
- 16 The recipe
- 17 Unified framework
- 18 SHAP's ingredients
- 19 Human-friendly
- 20 Game-theoretic
- 21 Information-theoretic
- 22 Choosing optimal combinations is hard
- 23 This is the perfect opportunity for predictive models
- 24 In high stakes scenarios, models should be interpretable
- 25 A simple fix: ensemble attributions
- 26 Interpretability uncovers transcriptional programs
- 27 Bringing Interpretable Models to Cancer Precision Medicine
- 28 Contrastive Analysis
- 29 Latent Variable Models
- 30 VAE Model
- 31 Problem
- 32 Contrastive Latent Variable Model
- 33 Background datasets
- 34 Contrastive VAE
- 35 Inspecting the salient latent values
