Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cross-Domain Federated Learning in Medical Imaging   Federated learning is increasingly being explored in the field of medical
imaging to train deep learning models on large scale datasets distributed
across different data centers while preserving privacy by avoiding the need to
transfer sensitive patient information. In this manuscript, we explore
federated learning in a multi-domain, multi-task setting wherein different
participating nodes may contain datasets sourced from different domains and are
trained to solve different tasks. We evaluated cross-domain federated learning
for the tasks of object detection and segmentation across two different
experimental settings: multi-modal and multi-organ. The result from our
experiments on cross-domain federated learning framework were very encouraging
with an overlap similarity of 0.79 for organ localization and 0.65 for lesion
segmentation. Our results demonstrate the potential of federated learning in
developing multi-domain, multi-task deep learning models without sharing data
from different domains.

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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
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The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: stat
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The categories of this paper are: cond-mat
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol