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⇱ Explainable Deep Learning Models for Healthcare | Coursera

Explainable Deep Learning Models for Healthcare

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Explainable Deep Learning Models for Healthcare

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Gain insight into a topic and learn the fundamentals.
4.6

15 reviews

Intermediate level

Recommended experience

3 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.6

15 reviews

Intermediate level

Recommended experience

3 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Program global explainability methods in time-series classification.

  • Program local explainability methods for deep learning such as CAM and GRAD-CAM.

  • Understand axiomatic attributions for deep learning networks.

  • Incorporate attention in Recurrent Neural Networks and visualise the attention weights.

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Assessments

5 assignments

Taught in English

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This course is part of the Informed Clinical Decision Making using Deep Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

This course will introduce the concepts of interpretability and explainability in machine learning applications. The learner will understand the difference between global, local, model-agnostic and model-specific explanations. State-of-the-art explainability methods such as Permutation Feature Importance (PFI), Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanation (SHAP) are explained and applied in time-series classification. Subsequently, model-specific explanations such as Class-Activation Mapping (CAM) and Gradient-Weighted CAM are explained and implemented. The learners will understand axiomatic attributions and why they are important. Finally, attention mechanisms are going to be incorporated after Recurrent Layers and the attention weights will be visualised to produce local explanations of the model.

Deep learning models are complex and it is difficult to understand their decisions. Explainability methods aim to shed light to the deep learning decisions and enhance trust, avoid mistakes and ensure ethical use of AI. Explanations can be categorised as global, local, model-agnostic and model-specific. Permutation feature importance is a global, model agnostic explainabillity method that provide information with relation to which input variables are more related to the output.

What's included

6 videos8 readings1 assignment1 discussion prompt5 ungraded labs

6 videos•Total 72 minutes
  • Welcome video - Explainable Deep Learning Models for Healthcare•1 minute
  • Interpretability vs Explainability•17 minutes
  • 'Explainability' in Healthcare Applications•17 minutes
  • Taxonomy of Explainability Methods•17 minutes
  • Model Agnostic Explainability Methods•11 minutes
  • Permutation Feature Importance in Time Series Data•8 minutes
8 readings•Total 390 minutes
  • The importance of explainable prediction models in healthcare•10 minutes
  • Explainable Artificial Intelligence - Taxonomy•60 minutes
  • Model Agnostic Explainability•30 minutes
  • Permutation Feature Importance•60 minutes
  • Practical Exercise: Interpretability of the MLP model using Permutation Feature Importance•60 minutes
  • Practical Exercise: Interpretability of the CNN model using Permutation Feature Importance•60 minutes
  • Practical Exercise: Interpretability of the LSTM model using Permutation Feature Importance•60 minutes
  • Explainability models in ECG•50 minutes
1 assignment•Total 30 minutes
  • End of week 1 quiz•30 minutes
1 discussion prompt•Total 10 minutes
  • Week 1 - Your experience•10 minutes
5 ungraded labs•Total 50 minutes
  • Permutation feature importance for classifying heart beats using a CNN•10 minutes
  • Light - Permutation feature importance for classifying heart beats using a CNN•10 minutes
  • Permutation feature importance for classifying heart beats using an LSTM•10 minutes
  • Light - Permutation feature importance for classifying heart beats using an LSTM•10 minutes
  • Permutation feature importance for classifying heart beats using a multi-layer perceptron•10 minutes

Local explainability methods provide explanations on how the model reach a specific decision. LIME approximates the model locally with a simpler, interpretable model. SHAP expands on this and it is also designed to address multi-collinearity of the input features. Both LIME and SHAP are local, model-agnostic explanations. On the other hand, CAM is a class-discriminative visualisation techniques, specifically designed to provide local explanations in deep neural networks.

What's included

5 videos7 readings1 assignment1 discussion prompt7 ungraded labs

5 videos•Total 48 minutes
  • Local Interpretable Model Agnostic Explanations (LIME)•9 minutes
  • LIME in Time-Series Classification•10 minutes
  • Shapley Additive Explanations•12 minutes
  • Model-Specific Explanations: Visualisation Methods•13 minutes
  • CAM in Time-Series Classification•5 minutes
7 readings•Total 320 minutes
  • Why Should I Trust You?•30 minutes
  • Practical Exercise: Interpretability of heartbeat classification using LIME and an NNMLP model•60 minutes
  • Practical Exercise: Interpretability of heartbeat classification using LIME and a CNN model•60 minutes
  • Practical Exercise: Interpretability of heartbeat classification using LIME and an LSTM model•60 minutes
  • A Unified Approach to Interpreting Model Predictions•40 minutes
  • Practical Exercise: Interpretability of CNN models using Class Activation Maps•10 minutes
  • Class Activation Mapping•60 minutes
1 assignment•Total 30 minutes
  • End of week 2 quiz•30 minutes
1 discussion prompt•Total 10 minutes
  • Week 2 - Your experience•10 minutes
7 ungraded labs•Total 65 minutes
  • Interpretability of heartbeat classification using a CNN model and Class Activation Maps•10 minutes
  • Interpretability of heartbeat classification using a CNN model and CAM•10 minutes
  • LIME interpretability for heartbeat classification with a convolutional neural network•10 minutes
  • Interpretability of heartbeat classification using an LSTM model and CAM•10 minutes
  • LIME interpretability for heartbeat classification with a long short-term memory network•10 minutes
  • Light - LIME interpretability for heartbeat classification with a long short-term memory network•5 minutes
  • LIME interpretability for heartbeat classification with a multi-layer perceptron•10 minutes

GRAD-CAM is an extension of CAM, which aims to a broader application of the architecture in deep neural networks. Although, it is one of the most popular methods in explaining deep neural network decisions, it violates key axiomatic properties, such as sensitivity and completeness. Integrated gradients is an axiomatic attribution method that aims to cover this gap.

What's included

4 videos6 readings1 assignment1 discussion prompt7 ungraded labs

4 videos•Total 37 minutes
  • Gradient Weighted Class Activation Maps•12 minutes
  • Grad-CAM in Time-Series Classification•9 minutes
  • Integrated Gradients•12 minutes
  • Integrated Gradients in Time Series Classification•4 minutes
6 readings•Total 310 minutes
  • GRAD - Class Activation Mapping •30 minutes
  • Practical Exercise: Interpretability of the CNN model using Gradient-weighted Class Activation Mapping•60 minutes
  • Practical Exercise: Interpretability of the LSTM model using Gradient-weighted Class Activation Mapping•60 minutes
  • Axiomatic Attribution for Deep Networks•40 minutes
  • Practical Exercise: Interpretability of the CNN model using Integrated Gradients•60 minutes
  • Practical Exercise: Interpretability of the LSTM model using Integrated Gradients•60 minutes
1 assignment•Total 30 minutes
  • End of week 3 quiz•30 minutes
1 discussion prompt•Total 10 minutes
  • Week 3 - Your experience•10 minutes
7 ungraded labs•Total 70 minutes
  • Interpretability of heartbeat classification using a CNN model and Grad-CAM•10 minutes
  • Interpretability of heartbeat classification using integrated gradients and a CNN model•10 minutes
  • Light - Interpretability of heartbeat classification using integrated gradients and a CNN model•10 minutes
  • Interpretability of heartbeat classification using an LSTM model and Grad-CAM•10 minutes
  • Light - Interpretability of heartbeat classification using an LSTM model and Grad-CAM•10 minutes
  • Interpretability of heartbeat classification using integrated gradients and an LSTM model•10 minutes
  • Light - Interpretability of heartbeat classification using integrated gradients and an LSTM model•10 minutes

Attention in deep neural networks mimics human attention that allocates computational resources to a small range of sensory input in order to process specific information with limited processing power. In this week, we discuss how to incorporate attention in Recurrent Neural Networks and autoencoders. Furthermore, we visualise attention weights in order to provide a form of inherent explanation for the decision making process.

What's included

3 videos3 readings2 assignments1 discussion prompt4 ungraded labs

3 videos•Total 34 minutes
  • Attention in Deep Learning•14 minutes
  • Taxonomy of Attention•13 minutes
  • Attention and Explainability•8 minutes
3 readings•Total 190 minutes
  • Survey on Attention Mechanisms•10 minutes
  • Practical Exercise: Classification of heartbeats using an LSTM with attention mechanism•90 minutes
  • Practical Exercise: Interpretability of the LSTM model with attention mechanism•90 minutes
2 assignments•Total 60 minutes
  • End of course summative quiz•30 minutes
  • End of week 4 quiz•30 minutes
1 discussion prompt•Total 10 minutes
  • Week 4 - Your experience•10 minutes
4 ungraded labs•Total 30 minutes
  • Interpretability of heartbeat classification using an LSTM model with attention mechanism•10 minutes
  • Light - Interpretability of heartbeat classification using an LSTM model with attention mechanism•5 minutes
  • Heartbeat classification using an LSTM model with attention mechanism•10 minutes
  • Light - Heartbeat classification using an LSTM model with attention mechanism•5 minutes

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Instructor

University of Glasgow
5 Courses•6,408 learners

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