Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Relevance Regularization: Interpretable and Robust Tumor Typing of
  Imaging Mass Spectrometry Data   Neural networks have recently been established as a viable classification
method for imaging mass spectrometry data for tumor typing. For
multi-laboratory scenarios however, certain confounding factors may strongly
impede their performance. In this work, we introduce Deep Relevance
Regularization, a method of restricting what the neural network can focus on
during classification, in order to improve the classification performance. We
demonstrate how Deep Relevance Regularization robustifies neural networks
against confounding factors on a challenging inter-lab dataset consisting of
breast and ovarian carcinoma. We further show that this makes the relevance map
-- a way of visualizing the discriminative parts of the mass spectrum --
sparser, thereby making the classifier easier to interpret

13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th