Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Analysis of the Influence of Transfer Learning When Measuring the
  Tortuosity of Blood Vessels   Characterizing blood vessels in digital images is important for the diagnosis
of many types of diseases as well as for assisting current researches regarding
vascular systems. The automated analysis of blood vessels typically requires
the identification, or segmentation, of the blood vessels in an image or a set
of images, which is usually a challenging task. Convolutional Neural Networks
(CNNs) have been shown to provide excellent results regarding the segmentation
of blood vessels. One important aspect of CNNs is that they can be trained on
large amounts of data and then be made available, for instance, in image
processing software for wide use. The pre-trained CNNs can then be easily
applied in downstream blood vessel characterization tasks such as the
calculation of the length, tortuosity, or caliber of the blood vessels. Yet, it
is still unclear if pre-trained CNNs can provide robust, unbiased, results on
downstream tasks when applied to datasets that they were not trained on. Here,
we focus on measuring the tortuosity of blood vessels and investigate to which
extent CNNs may provide biased tortuosity values even after fine-tuning the
network to the new dataset under study. We show that the tortuosity values
obtained by a CNN trained from scratch on a dataset may not agree with those
obtained by a fine-tuned network that was pre-trained on a dataset having
different tortuosity statistics. In addition, we show that the improvement in
segmentation performance when fine-tuning the network does not necessarily lead
to a respective improvement on the estimation of the tortuosity. To mitigate
the aforementioned issues, we propose the application of specific data
augmentation techniques even in situations where they do not improve
segmentation performance.

33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat