Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Resolution Networks for Semantic Segmentation in Whole Slide
  Images   Digital pathology provides an excellent opportunity for applying fully
convolutional networks (FCNs) to tasks, such as semantic segmentation of whole
slide images (WSIs). However, standard FCNs face challenges with respect to
multi-resolution, inherited from the pyramid arrangement of WSIs. As a result,
networks specifically designed to learn and aggregate information at different
levels are desired. In this paper, we propose two novel multi-resolution
networks based on the popular `U-Net' architecture, which are evaluated on a
benchmark dataset for binary semantic segmentation in WSIs. The proposed
methods outperform the U-Net, demonstrating superior learning and
generalization capabilities.

30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int