Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convex Shape Prior for Deep Neural Convolution Network based Eye Fundus
  Images Segmentation   Convex Shapes (CS) are common priors for optic disc and cup segmentation in
eye fundus images. It is important to design proper techniques to represent
convex shapes. So far, it is still a problem to guarantee that the output
objects from a Deep Neural Convolution Networks (DCNN) are convex shapes. In
this work, we propose a technique which can be easily integrated into the
commonly used DCNNs for image segmentation and guarantee that outputs are
convex shapes. This method is flexible and it can handle multiple objects and
allow some of the objects to be convex. Our method is based on the dual
representation of the sigmoid activation function in DCNNs. In the dual space,
the convex shape prior can be guaranteed by a simple quadratic constraint on a
binary representation of the shapes. Moreover, our method can also integrate
spatial regularization and some other shape prior using a soft thresholding
dynamics (STD) method. The regularization can make the boundary curves of the
segmentation objects to be simultaneously smooth and convex. We design a very
stable active set projection algorithm to numerically solve our model. This
algorithm can form a new plug-and-play DCNN layer called CS-STD whose outputs
must be a nearly binary segmentation of convex objects. In the CS-STD block,
the convexity information can be propagated to guide the DCNN in both forward
and backward propagation during training and prediction process. As an
application example, we apply the convexity prior layer to the retinal fundus
images segmentation by taking the popular DeepLabV3+ as a backbone network.
Experimental results on several public datasets show that our method is
efficient and outperforms the classical DCNN segmentation methods.

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The categories of this paper are: physics
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The categories of this paper are: mtrl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: nlin
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
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The categories of this paper are: astro-ph
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The categories of this paper are: math
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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph