Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: StairwayGraphNet for Inter- and Intra-modality Multi-resolution Brain
  Graph Alignment and Synthesis   Synthesizing multimodality medical data provides complementary knowledge and
helps doctors make precise clinical decisions. Although promising, existing
multimodal brain graph synthesis frameworks have several limitations. First,
they mainly tackle only one problem (intra- or inter-modality), limiting their
generalizability to synthesizing inter- and intra-modality simultaneously.
Second, while few techniques work on super-resolving low-resolution brain
graphs within a single modality (i.e., intra), inter-modality graph
super-resolution remains unexplored though this would avoid the need for costly
data collection and processing. More importantly, both target and source
domains might have different distributions, which causes a domain fracture
between them. To fill these gaps, we propose a multi-resolution
StairwayGraphNet (SG-Net) framework to jointly infer a target graph modality
based on a given modality and super-resolve brain graphs in both inter and
intra domains. Our SG-Net is grounded in three main contributions: (i)
predicting a target graph from a source one based on a novel graph generative
adversarial network in both inter (e.g., morphological-functional) and intra
(e.g., functional-functional) domains, (ii) generating high-resolution brain
graphs without resorting to the time consuming and expensive MRI processing
steps, and (iii) enforcing the source distribution to match that of the ground
truth graphs using an inter-modality aligner to relax the loss function to
optimize. Moreover, we design a new Ground Truth-Preserving loss function to
guide both generators in learning the topological structure of ground truth
brain graphs more accurately. Our comprehensive experiments on predicting
target brain graphs from source graphs using a multi-resolution stairway showed
the outperformance of our method in comparison with its variants and
state-of-the-art method.

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The categories of this paper are: comp-gas
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: alg-geom
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The categories of this paper are: stat
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The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: physics