Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Assessing the Role of Random Forests in Medical Image Segmentation   Neural networks represent a field of research that can quickly achieve very
good results in the field of medical image segmentation using a GPU. A possible
way to achieve good results without GPUs are random forests. For this purpose,
two random forest approaches were compared with a state-of-the-art deep
convolutional neural network. To make the comparison the PhC-C2DH-U373 and the
retinal imaging datasets were used. The evaluation showed that the deep
convolutional neutral network achieved the best results. However, one of the
random forest approaches also achieved a similar high performance. Our results
indicate that random forest approaches are a good alternative to deep
convolutional neural networks and, thus, allow the usage of medical image
segmentation without a GPU.

28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci