Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attention Enriched Deep Learning Model for Breast Tumor Segmentation in
  Ultrasound Images   Incorporating human domain knowledge for breast tumor diagnosis is
challenging, since shape, boundary, curvature, intensity, or other common
medical priors vary significantly across patients and cannot be employed. This
work proposes a new approach for integrating visual saliency into a deep
learning model for breast tumor segmentation in ultrasound images. Visual
saliency refers to image maps containing regions that are more likely to
attract radiologists visual attention. The proposed approach introduces
attention blocks into a U-Net architecture, and learns feature representations
that prioritize spatial regions with high saliency levels. The validation
results demonstrate increased accuracy for tumor segmentation relative to
models without salient attention layers. The approach achieved a Dice
similarity coefficient of 90.5 percent on a dataset of 510 images. The salient
attention model has potential to enhance accuracy and robustness in processing
medical images of other organs, by providing a means to incorporate
task-specific knowledge into deep learning architectures.

9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th