Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few\n Labels Article Swipe
Valentyn Melnychuk
,
Evgeniy Faerman
,
Ilja Manakov
,
Thomas Seidl
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2010.12316
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2010.12316
Unlabeled data is often abundant in the clinic, making machine learning\nmethods based on semi-supervised learning a good match for this setting.\nDespite this, they are currently receiving relatively little attention in\nmedical image analysis literature. Instead, most practitioners and researchers\nfocus on supervised or transfer learning approaches. The recently proposed\nMixMatch and FixMatch algorithms have demonstrated promising results in\nextracting useful representations while requiring very few labels. Motivated by\nthese recent successes, we apply MixMatch and FixMatch in an ophthalmological\ndiagnostic setting and investigate how they fare against standard transfer\nlearning. We find that both algorithms outperform the transfer learning\nbaseline on all fractions of labelled data. Furthermore, our experiments show\nthat exponential moving average (EMA) of model parameters, which is a component\nof both algorithms, is not needed for our classification problem, as disabling\nit leaves the outcome unchanged. Our code is available online:\nhttps://github.com/Valentyn1997/oct-diagn-semi-supervised\n
Related Topics
Concepts
Transfer of learning
Computer science
Focus (optics)
Artificial intelligence
Machine learning
Matching (statistics)
Code (set theory)
Supervised learning
Component (thermodynamics)
Baseline (sea)
Semi-supervised learning
Labeled data
Pattern recognition (psychology)
Mathematics
Statistics
Artificial neural network
Oceanography
Geology
Set (abstract data type)
Optics
Physics
Programming language
Thermodynamics
Metadata
- Type
- preprint
- Landing Page
- http://arxiv.org/abs/2010.12316
- https://arxiv.org/pdf/2010.12316
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4287634253
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4287634253Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2010.12316Digital Object Identifier
- Title
-
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few\n LabelsWork title
- Type
-
preprintOpenAlex work type
- Publication year
-
2020Year of publication
- Publication date
-
2020-10-23Full publication date if available
- Authors
-
Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov, Thomas SeidlList of authors in order
- Landing page
-
https://arxiv.org/abs/2010.12316Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2010.12316Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2010.12316Direct OA link when available
- Concepts
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Transfer of learning, Computer science, Focus (optics), Artificial intelligence, Machine learning, Matching (statistics), Code (set theory), Supervised learning, Component (thermodynamics), Baseline (sea), Semi-supervised learning, Labeled data, Pattern recognition (psychology), Mathematics, Statistics, Artificial neural network, Oceanography, Geology, Set (abstract data type), Optics, Physics, Programming language, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.relatively | 26 |
| abstract_inverted_index.show\nthat | 101 |
| abstract_inverted_index.successes, | 65 |
| abstract_inverted_index.supervised | 39 |
| abstract_inverted_index.unchanged. | 127 |
| abstract_inverted_index.algorithms, | 114 |
| abstract_inverted_index.approaches. | 43 |
| abstract_inverted_index.experiments | 100 |
| abstract_inverted_index.exponential | 102 |
| abstract_inverted_index.in\nmedical | 29 |
| abstract_inverted_index.investigate | 76 |
| abstract_inverted_index.literature. | 32 |
| abstract_inverted_index.parameters, | 108 |
| abstract_inverted_index.Furthermore, | 98 |
| abstract_inverted_index.demonstrated | 51 |
| abstract_inverted_index.component\nof | 112 |
| abstract_inverted_index.disabling\nit | 123 |
| abstract_inverted_index.practitioners | 35 |
| abstract_inverted_index.classification | 120 |
| abstract_inverted_index.in\nextracting | 54 |
| abstract_inverted_index.representations | 56 |
| abstract_inverted_index.semi-supervised | 13 |
| abstract_inverted_index.learning\nmethods | 10 |
| abstract_inverted_index.setting.\nDespite | 20 |
| abstract_inverted_index.learning\nbaseline | 91 |
| abstract_inverted_index.proposed\nMixMatch | 46 |
| abstract_inverted_index.researchers\nfocus | 37 |
| abstract_inverted_index.transfer\nlearning. | 82 |
| abstract_inverted_index.ophthalmological\ndiagnostic | 73 |
| abstract_inverted_index.online:\nhttps://github.com/Valentyn1997/oct-diagn-semi-supervised\n | 132 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.35549128 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |