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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.

portfolio

publications

Prior notification of trial participants by newsletter increased response rates: a randomized controlled trial.

Published in Journal of Clinical Epidemiology, 2012

My personal contribution was creating the meta analysis for the trial.

Recommended citation: Natasha Mitchell, Catherine E. Hewitt, Elizabeth Lenaghan, Eleanor Platt, Lee Shepstone, David J. Torgerson, on behalf of the SCOOP study team. (2012). "Prior notification of trial participants by newsletter increased response rates: a randomized controlled trial." Journal 1. 1(1).

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.