Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical brain parcellation with uncertainty   Many atlases used for brain parcellation are hierarchically organised,
progressively dividing the brain into smaller sub-regions. However,
state-of-the-art parcellation methods tend to ignore this structure and treat
labels as if they are `flat'. We introduce a hierarchically-aware brain
parcellation method that works by predicting the decisions at each branch in
the label tree. We further show how this method can be used to model
uncertainty separately for every branch in this label tree. Our method exceeds
the performance of flat uncertainty methods, whilst also providing decomposed
uncertainty estimates that enable us to obtain self-consistent parcellations
and uncertainty maps at any level of the label hierarchy. We demonstrate a
simple way these decision-specific uncertainty maps may be used to provided
uncertainty-thresholded tissue maps at any level of the label tree.

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The categories of this paper are: q-fin
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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
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The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph