Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: How Much Off-The-Shelf Knowledge Is Transferable From Natural Images To
  Pathology Images?   Deep learning has achieved a great success in natural image classification.
To overcome data-scarcity in computational pathology, recent studies exploit
transfer learning to reuse knowledge gained from natural images in pathology
image analysis, aiming to build effective pathology image diagnosis models.
Since transferability of knowledge heavily depends on the similarity of the
original and target tasks, significant differences in image content and
statistics between pathology images and natural images raise the questions: how
much knowledge is transferable? Is the transferred information equally
contributed by pre-trained layers? To answer these questions, this paper
proposes a framework to quantify knowledge gain by a particular layer, conducts
an empirical investigation in pathology image centered transfer learning, and
reports some interesting observations. Particularly, compared to the
performance baseline obtained by random-weight model, though transferability of
off-the-shelf representations from deep layers heavily depend on specific
pathology image sets, the general representation generated by early layers does
convey transferred knowledge in various image classification applications. The
observation in this study encourages further investigation of specific metric
and tools to quantify effectiveness and feasibility of transfer learning in
future.

34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin