Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Domain Generalization for Mammography Detection via Multi-style and
  Multi-view Contrastive Learning   Lesion detection is a fundamental problem in the computer-aided diagnosis
scheme for mammography. The advance of deep learning techniques have made a
remarkable progress for this task, provided that the training data are large
and sufficiently diverse in terms of image style and quality. In particular,
the diversity of image style may be majorly attributed to the vendor factor.
However, the collection of mammograms from vendors as many as possible is very
expensive and sometimes impractical for laboratory-scale studies. Accordingly,
to further augment the generalization capability of deep learning model to
various vendors with limited resources, a new contrastive learning scheme is
developed. Specifically, the backbone network is firstly trained with a
multi-style and multi-view unsupervised self-learning scheme for the embedding
of invariant features to various vendor-styles. Afterward, the backbone network
is then recalibrated to the downstream task of lesion detection with the
specific supervised learning. The proposed method is evaluated with mammograms
from four vendors and one unseen public dataset. The experimental results
suggest that our approach can effectively improve detection performance on both
seen and unseen domains, and outperforms many state-of-the-art (SOTA)
generalization methods.

31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph