Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Radiance Projection   The proposed method, Neural Radiance Projection (NeRP), addresses the three
most fundamental shortages of training such a convolutional neural network on
X-ray image segmentation: dealing with missing/limited human-annotated
datasets; ambiguity on the per-pixel label; and the imbalance across positive-
and negative- classes distribution. By harnessing a generative adversarial
network, we can synthesize a massive amount of physics-based X-ray images,
so-called Variationally Reconstructed Radiographs (VRRs), alongside their
segmentation from more accurate labeled 3D Computed Tomography data. As a
result, VRRs present more faithfully than other projection methods in terms of
photo-realistic metrics. Adding outputs from NeRP also surpasses the vanilla
UNet models trained on the same pairs of X-ray images.

17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
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The categories of this paper are: cmp-lg
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The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: stat
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The categories of this paper are: adap-org
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The categories of this paper are: econ