Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiclass segmentation as multitask learning for drusen segmentation in
  retinal optical coherence tomography   Automated drusen segmentation in retinal optical coherence tomography (OCT)
scans is relevant for understanding age-related macular degeneration (AMD) risk
and progression. This task is usually performed by segmenting the top/bottom
anatomical interfaces that define drusen, the outer boundary of the retinal
pigment epithelium (OBRPE) and the Bruch's membrane (BM), respectively. In this
paper we propose a novel multi-decoder architecture that tackles drusen
segmentation as a multitask problem. Instead of training a multiclass model for
OBRPE/BM segmentation, we use one decoder per target class and an extra one
aiming for the area between the layers. We also introduce connections between
each class-specific branch and the additional decoder to increase the
regularization effect of this surrogate task. We validated our approach on
private/public data sets with 166 early/intermediate AMD Spectralis, and 200
AMD and control Bioptigen OCT volumes, respectively. Our method consistently
outperformed several baselines in both layer and drusen segmentation
evaluations.

17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas