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Thesis

Fuzzy and probabilistic segmentation, and appropriate validation, applied to cardiac magnetic resonance images

Abstract:

Algorithms producing fuzzy and probabilistic (i.e. 'soft') segmentations are becoming increasingly popular. However, many of the unique strengths of such algorithms get overlooked, especially when used in the context of deterministic frameworks, which typically treat softness as label uncertainty, and tend to discard it as the final step. We maintain that such treatment results in loss of potentially useful information, which could be used to improve outcomes further. This is particularly ...

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Department:
University of Oxford
Role:
Supervisor
Department:
University of Leeds
Role:
Supervisor
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
Language:
English
UUID:
uuid:dc352697-c804-4257-8aec-088ea28806c5
Deposit date:
2018-01-08

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