Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Focal Attention Networks: optimising attention for biomedical image
  segmentation   In recent years, there has been increasing interest to incorporate attention
into deep learning architectures for biomedical image segmentation. The modular
design of attention mechanisms enables flexible integration into convolutional
neural network architectures, such as the U-Net. Whether attention is
appropriate to use, what type of attention to use, and where in the network to
incorporate attention modules, are all important considerations that are
currently overlooked. In this paper, we investigate the role of the Focal
parameter in modulating attention, revealing a link between attention in loss
functions and networks. By incorporating a Focal distance penalty term, we
extend the Unified Focal loss framework to include boundary-based losses.
Furthermore, we develop a simple and interpretable, dataset and model-specific
heuristic to integrate the Focal parameter into the Squeeze-and-Excitation
block and Attention Gate, achieving optimal performance with fewer number of
attention modules on three well-validated biomedical imaging datasets,
suggesting judicious use of attention modules results in better performance and
efficiency.

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The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph