Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Image Labels On-the-fly for Training Robust Classification
  Models   Current deep learning paradigms largely benefit from the tremendous amount of
annotated data. However, the quality of the annotations often varies among
labelers. Multi-observer studies have been conducted to study these annotation
variances (by labeling the same data for multiple times) and its effects on
critical applications like medical image analysis. This process indeed adds an
extra burden to the already tedious annotation work that usually requires
professional training and expertise in the specific domains. On the other hand,
automated annotation methods based on NLP algorithms have recently shown
promise as a reasonable alternative, relying on the existing diagnostic reports
of those images that are widely available in the clinical system. Compared to
human labelers, different algorithms provide labels with varying qualities that
are even noisier. In this paper, we show how noisy annotations (e.g., from
different algorithm-based labelers) can be utilized together and mutually
benefit the learning of classification tasks. Specifically, the concept of
attention-on-label is introduced to sample better label sets on-the-fly as the
training data. A meta-training based label-sampling module is designed to
attend the labels that benefit the model learning the most through additional
back-propagation processes. We apply the attention-on-label scheme on the
classification task of a synthetic noisy CIFAR-10 dataset to prove the concept,
and then demonstrate superior results (3-5% increase on average in multiple
disease classification AUCs) on the chest x-ray images from a hospital-scale
dataset (MIMIC-CXR) and hand-labeled dataset (OpenI) in comparison to regular
training paradigms.

12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat