Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adapting to Unseen Vendor Domains for MRI Lesion Segmentation   One of the key limitations in machine learning models is poor performance on
data that is out of the domain of the training distribution. This is especially
true for image analysis in magnetic resonance (MR) imaging, as variations in
hardware and software create non-standard intensities, contrasts, and noise
distributions across scanners. Recently, image translation models have been
proposed to augment data across domains to create synthetic data points. In
this paper, we investigate the application an unsupervised image translation
model to augment MR images from a source dataset to a target dataset.
Specifically, we want to evaluate how well these models can create synthetic
data points representative of the target dataset through image translation, and
to see if a segmentation model trained these synthetic data points would
approach the performance of a model trained directly on the target dataset. We
consider three configurations of augmentation between datasets consisting of
translation between images, between scanner vendors, and from labels to images.
It was found that the segmentation models trained on synthetic data from labels
to images configuration yielded the closest performance to the segmentation
model trained directly on the target dataset. The Dice coeffcient score per
each target vendor (GE, Siemens, Philips) for training on synthetic data was
0.63, 0.64, and 0.58, compared to training directly on target dataset was 0.65,
0.72, and 0.61.

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The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
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The categories of this paper are: supr-con
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
0
The categories of this paper are: hep-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: gr-qc
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph