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digital health

explainability

Interpretable and Explainable Deep Learning for Image Processing

8 minute read

Published:

Interpretability, explainability, or the lack of either, have been popular talking points in the deep learning community. The terms are generally ill-defined and the black-box nature of models, such as deep neural networks, makes it challenging to harness their power in real world applications. In this blog post, I explore notions of interpretability and explainability for both humans and machines, and discuss how we can even begin to make progress towards truly interpretable or explainable algorithms for image processing.

hackathon

interpretability

Interpretable and Explainable Deep Learning for Image Processing

8 minute read

Published:

Interpretability, explainability, or the lack of either, have been popular talking points in the deep learning community. The terms are generally ill-defined and the black-box nature of models, such as deep neural networks, makes it challenging to harness their power in real world applications. In this blog post, I explore notions of interpretability and explainability for both humans and machines, and discuss how we can even begin to make progress towards truly interpretable or explainable algorithms for image processing.

medical imaging

Interpretable and Explainable Deep Learning for Image Processing

8 minute read

Published:

Interpretability, explainability, or the lack of either, have been popular talking points in the deep learning community. The terms are generally ill-defined and the black-box nature of models, such as deep neural networks, makes it challenging to harness their power in real world applications. In this blog post, I explore notions of interpretability and explainability for both humans and machines, and discuss how we can even begin to make progress towards truly interpretable or explainable algorithms for image processing.

product forge

trustworthy

Interpretable and Explainable Deep Learning for Image Processing

8 minute read

Published:

Interpretability, explainability, or the lack of either, have been popular talking points in the deep learning community. The terms are generally ill-defined and the black-box nature of models, such as deep neural networks, makes it challenging to harness their power in real world applications. In this blog post, I explore notions of interpretability and explainability for both humans and machines, and discuss how we can even begin to make progress towards truly interpretable or explainable algorithms for image processing.