Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: METGAN: Generative Tumour Inpainting and Modality Synthesis in Light
  Sheet Microscopy   Novel multimodal imaging methods are capable of generating extensive, super
high resolution datasets for preclinical research. Yet, a massive lack of
annotations prevents the broad use of deep learning to analyze such data. So
far, existing generative models fail to mitigate this problem because of
frequent labeling errors. In this paper, we introduce a novel generative method
which leverages real anatomical information to generate realistic image-label
pairs of tumours. We construct a dual-pathway generator, for the anatomical
image and label, trained in a cycle-consistent setup, constrained by an
independent, pretrained segmentor. The generated images yield significant
quantitative improvement compared to existing methods. To validate the quality
of synthesis, we train segmentation networks on a dataset augmented with the
synthetic data, substantially improving the segmentation over baseline.

20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph