Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training Multi-organ Segmentation Networks with Sample Selection by
  Relaxed Upper Confident Bound   Deep convolutional neural networks (CNNs), especially fully convolutional
networks, have been widely applied to automatic medical image segmentation
problems, e.g., multi-organ segmentation. Existing CNN-based segmentation
methods mainly focus on looking for increasingly powerful network
architectures, but pay less attention to data sampling strategies for training
networks more effectively. In this paper, we present a simple but effective
sample selection method for training multi-organ segmentation networks. Sample
selection exhibits an exploitation-exploration strategy, i.e., exploiting hard
samples and exploring less frequently visited samples. Based on the fact that
very hard samples might have annotation errors, we propose a new sample
selection policy, named Relaxed Upper Confident Bound (RUCB). Compared with
other sample selection policies, e.g., Upper Confident Bound (UCB), it exploits
a range of hard samples rather than being stuck with a small set of very hard
ones, which mitigates the influence of annotation errors during training. We
apply this new sample selection policy to training a multi-organ segmentation
network on a dataset containing 120 abdominal CT scans and show that it boosts
segmentation performance significantly.

21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th