Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few
  Labels   Unlabeled data is often abundant in the clinic, making machine learning
methods based on semi-supervised learning a good match for this setting.
Despite this, they are currently receiving relatively little attention in
medical image analysis literature. Instead, most practitioners and researchers
focus on supervised or transfer learning approaches. The recently proposed
MixMatch and FixMatch algorithms have demonstrated promising results in
extracting useful representations while requiring very few labels. Motivated by
these recent successes, we apply MixMatch and FixMatch in an ophthalmological
diagnostic setting and investigate how they fare against standard transfer
learning. We find that both algorithms outperform the transfer learning
baseline on all fractions of labelled data. Furthermore, our experiments show
that exponential moving average (EMA) of model parameters, which is a component
of both algorithms, is not needed for our classification problem, as disabling
it leaves the outcome unchanged. Our code is available online:
https://github.com/Valentyn1997/oct-diagn-semi-supervised

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The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
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The categories of this paper are: q-fin
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The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci