Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Segmentation of Macular Edema Datasets with Small Residual 3D U-Net
  Architectures   This paper investigates the application of deep convolutional neural networks
with prohibitively small datasets to the problem of macular edema segmentation.
In particular, we investigate several different heavily regularized
architectures. We find that, contrary to popular belief, neural architectures
within this application setting are able to achieve close to human-level
performance on unseen test images without requiring large numbers of training
examples. Annotating these 3D datasets is difficult, with multiple criteria
required. It takes an experienced clinician two days to annotate a single 3D
image, whereas our trained model achieves similar performance in less than a
second. We found that an approach which uses targeted dataset augmentation,
alongside architectural simplification with an emphasis on residual design, has
acceptable generalization performance - despite relying on fewer than 15
training examples.

17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat