Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Efficient Confidence Measure-Based Evaluation Metric for Breast
  Cancer Screening Using Bayesian Neural Networks   Screening mammograms is the gold standard for detecting breast cancer early.
While a good amount of work has been performed on mammography image
classification, especially with deep neural networks, there has not been much
exploration into the confidence or uncertainty measurement of the
classification. In this paper, we propose a confidence measure-based evaluation
metric for breast cancer screening. We propose a modular network architecture,
where a traditional neural network is used as a feature extractor with transfer
learning, followed by a simple Bayesian neural network. Utilizing a two-stage
approach helps reducing the computational complexity, making the proposed
framework attractive for wider deployment. We show that by providing the
medical practitioners with a tool to tune two hyperparameters of the Bayesian
neural network, namely, fraction of sampled number of networks and minimum
probability, the framework can be adapted as needed by the domain expert.
Finally, we argue that instead of just a single number such as accuracy, a
tuple (accuracy, coverage, sampled number of networks, and minimum probability)
can be utilized as an evaluation metric of our framework. We provide
experimental results on the CBIS-DDSM dataset, where we show the trends in
accuracy-coverage tradeoff while tuning the two hyperparameters. We also show
that our confidence tuning results in increased accuracy with a reduced set of
images with high confidence when compared to the baseline transfer learning. To
make the proposed framework readily deployable, we provide (anonymized) source
code with reproducible results at https://git.io/JvRqE.

31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys