Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Vendor-independent soft tissue lesion detection using weakly supervised
  and unsupervised adversarial domain adaptation   Computer-aided detection aims to improve breast cancer screening programs by
helping radiologists to evaluate digital mammography (DM) exams. DM exams are
generated by devices from different vendors, with diverse characteristics
between and even within vendors. Physical properties of these devices and
postprocessing of the images can greatly influence the resulting mammogram.
This results in the fact that a deep learning model trained on data from one
vendor cannot readily be applied to data from another vendor. This paper
investigates the use of tailored transfer learning methods based on adversarial
learning to tackle this problem. We consider a database of DM exams (mostly
bilateral and two views) generated by Hologic and Siemens vendors. We analyze
two transfer learning settings: 1) unsupervised transfer, where Hologic data
with soft lesion annotation at pixel level and Siemens unlabelled data are used
to annotate images in the latter data; 2) weak supervised transfer, where exam
level labels for images from the Siemens mammograph are available. We propose
tailored variants of recent state-of-the-art methods for transfer learning
which take into account the class imbalance and incorporate knowledge provided
by the annotations at exam level. Results of experiments indicate the
beneficial effect of transfer learning in both transfer settings. Notably, at
0.02 false positives per image, we achieve a sensitivity of 0.37, compared to
0.30 of a baseline with no transfer. Results indicate that using exam level
annotations gives an additional increase in sensitivity.

5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat