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Out-of-distribution detection in dermatology

Resource type
Thesis type
(Thesis) M.Sc.
Date created
2021-09-01
Authors/Contributors
Abstract
Deep neural networks are able to diagnose skin diseases from images accurately, but they still have a long way to go to be trusted in healthcare. In this thesis, we tackle two well-known problems in dermatology using out-of-distribution detection with deep neural networks. First, we show that by making small changes to the neural network model, we can make it capable of doing multi-class classification and novel-class detection at the same time. Then, we show that by combining lesion detection, lesion segmentation, and outlier detection modules, it is possible to build a pipeline capable of identifying suspicious ugly duckling signs from total body photography images.
Document
Extent
34 pages.
Identifier
etd21651
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Atkins, Stella
Language
English
Member of collection
Download file Size
etd21651.pdf 7.48 MB

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