Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Minimizing false negative rate in melanoma detection and providing
  insight into the causes of classification   Our goal is to bridge human and machine intelligence in melanoma detection.
We develop a classification system exploiting a combination of visual
pre-processing, deep learning, and ensembling for providing explanations to
experts and to minimize false negative rate while maintaining high accuracy in
melanoma detection. Source images are first automatically segmented using a
U-net CNN. The result of the segmentation is then used to extract image
sub-areas and specific parameters relevant in human evaluation, namely center,
border, and asymmetry measures. These data are then processed by tailored
neural networks which include structure searching algorithms. Partial results
are then ensembled by a committee machine. Our evaluation on the largest skin
lesion dataset which is publicly available today, ISIC-2019, shows improvement
in all evaluated metrics over a baseline using the original images only. We
also showed that indicative scores computed by the feature classifiers can
provide useful insight into the various features on which the decision can be
based.

18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph