Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Incorporating Task-Specific Structural Knowledge into CNNs for Brain
  Midline Shift Detection   Midline shift (MLS) is a well-established factor used for outcome prediction
in traumatic brain injury, stroke and brain tumors. The importance of automatic
estimation of MLS was recently highlighted by ACR Data Science Institute. In
this paper we introduce a novel deep learning based approach for the problem of
MLS detection, which exploits task-specific structural knowledge. We evaluate
our method on a large dataset containing heterogeneous images with significant
MLS and show that its mean error approaches the inter-expert variability.
Finally, we show the robustness of our approach by validating it on an external
dataset, acquired during routine clinical practice.

24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph