Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Guiding CNNs towards Relevant Concepts by Multi-task and Adversarial
  Learning   The opaqueness of deep learning limits its deployment in critical application
scenarios such as cancer grading in medical images. In this paper, a framework
for guiding CNN training is built on top of successful existing techniques of
hard parameter sharing, with the main goal of explicitly introducing expert
knowledge in the training objectives. The learning process is guided by
identifying concepts that are relevant or misleading for the task. Relevant
concepts are encouraged to appear in the representation through multi-task
learning. Undesired and misleading concepts are discouraged by a gradient
reversal operation. In this way, a shift in the deep representations can be
corrected to match the clinicians' assumptions. The application on breast lymph
nodes histopathology data from the Camelyon challenge shows a significant
increase in the generalization performance on unseen patients (from 0.839 to
0.864 average AUC, $\text{p-value} = 0,0002$) when the internal representations
are controlled by prior knowledge regarding the acquisition center and visual
features of the tissue. The code will be shared for reproducibility on our
GitHub repository.

17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con