Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: U-Net-and-a-half: Convolutional network for biomedical image
  segmentation using multiple expert-driven annotations   Development of deep learning systems for biomedical segmentation often
requires access to expert-driven, manually annotated datasets. If more than a
single expert is involved in the annotation of the same images, then the
inter-expert agreement is not necessarily perfect, and no single expert
annotation can precisely capture the so-called ground truth of the regions of
interest on all images. Also, it is not trivial to generate a reference
estimate using annotations from multiple experts. Here we present a deep neural
network, defined as U-Net-and-a-half, which can simultaneously learn from
annotations performed by multiple experts on the same set of images.
U-Net-and-a-half contains a convolutional encoder to generate features from the
input images, multiple decoders that allow simultaneous learning from image
masks obtained from annotations that were independently generated by multiple
experts, and a shared low-dimensional feature space. To demonstrate the
applicability of our framework, we used two distinct datasets from digital
pathology and radiology, respectively. Specifically, we trained two separate
models using pathologist-driven annotations of glomeruli on whole slide images
of human kidney biopsies (10 patients), and radiologist-driven annotations of
lumen cross-sections of human arteriovenous fistulae obtained from
intravascular ultrasound images (10 patients), respectively. The models based
on U-Net-and-a-half exceeded the performance of the traditional U-Net models
trained on single expert annotations alone, thus expanding the scope of
multitask learning in the context of biomedical image segmentation.

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The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph