Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to estimate label uncertainty for automatic radiology report
  parsing   Bootstrapping labels from radiology reports has become the scalable
alternative to provide inexpensive ground truth for medical imaging. Because of
the domain specific nature, state-of-the-art report labeling tools are
predominantly rule-based. These tools, however, typically yield a binary 0 or 1
prediction that indicates the presence or absence of abnormalities. These hard
targets are then used as ground truth to train image models in the downstream,
forcing models to express high degree of certainty even on cases where
specificity is low. This could negatively impact the statistical efficiency of
image models. We address such an issue by training a Bidirectional Long-Short
Term Memory Network to augment heuristic-based discrete labels of X-ray reports
from all body regions and achieve performance comparable or better than
domain-specific NLP, but with additional uncertainty estimates which enable
finer downstream image model training.

10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat