University of Glasgow
Explainable deep learning models for healthcare - CDSS 3
University of Glasgow

Explainable deep learning models for healthcare - CDSS 3

Fani Deligianni

Instructor: Fani Deligianni

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Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

30 hours (approximately)
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

Details to know

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Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

30 hours (approximately)
Flexible schedule
Learn at your own pace

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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

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 quiz1 discussion prompt5 ungraded labs

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 quiz1 discussion prompt7 ungraded labs

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 quiz1 discussion prompt7 ungraded labs

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 quizzes1 discussion prompt4 ungraded labs

Instructor

Fani Deligianni
University of Glasgow
5 Courses3,862 learners

Offered by

Recommended if you're interested in Machine Learning

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