Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Look, Investigate, and Classify: A Deep Hybrid Attention Method for
  Breast Cancer Classification   One issue with computer based histopathology image analysis is that the size
of the raw image is usually very large. Taking the raw image as input to the
deep learning model would be computationally expensive while resizing the raw
image to low resolution would incur information loss. In this paper, we present
a novel deep hybrid attention approach to breast cancer classification. It
first adaptively selects a sequence of coarse regions from the raw image by a
hard visual attention algorithm, and then for each such region it is able to
investigate the abnormal parts based on a soft-attention mechanism. A recurrent
network is then built to make decisions to classify the image region and also
to predict the location of the image region to be investigated at the next time
step. As the region selection process is non-differentiable, we optimize the
whole network through a reinforcement approach to learn an optimal policy to
classify the regions. Based on this novel Look, Investigate and Classify
approach, we only need to process a fraction of the pixels in the raw image
resulting in significant saving in computational resources without sacrificing
performances. Our approach is evaluated on a public breast cancer
histopathology database, where it demonstrates superior performance to the
state-of-the-art deep learning approaches, achieving around 96\% classification
accuracy while only 15% of raw pixels are used.

17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
16
The categories of this paper are: stat