Finding the semantic similarity in single-particle diffraction images using self-supervised contrastive projection learning Article Swipe
Julian Zimmermann
,
Fabien Beguet
,
Daniel Guthruf
,
Bruno Langbehn
,
Daniela Rupp
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3929/ethz-b-000599214
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3929/ethz-b-000599214
Single-shot diffraction imaging of isolated nanosized particles has seen remarkable success in recent years, yielding in-situ measurements with ultra-high spatial and temporal resolution. The progress of high-repetition-rate sources for intense X-ray pulses has further enabled recording datasets containing millions of diffraction images, which are needed for structure determination of specimens with greater structural variety and for dynamic experiments. The size of the datasets, however, represents a monumental problem for their analysis. Here, we present an automatized approach for finding semantic similarities in coherent diffraction images without relying on human expert labeling. By introducing the concept of projection learning, we extend self-supervised contrastive learning to the context of coherent diffraction imaging. As a result, we achieve a semantic dimensionality reduction producing meaningful embeddings that align with the physical intuition of an experienced human researcher. The method yields a substantial improvement compared to previous approaches, paving the way toward real-time and large-scale analysis of coherent diffraction experiments at X-ray free-electron lasers.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2208.11752
- https://arxiv.org/pdf/2208.11752
- OA Status
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4293332742Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3929/ethz-b-000599214Digital Object Identifier
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Finding the semantic similarity in single-particle diffraction images using self-supervised contrastive projection learningWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2023Year of publication
- Publication date
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2023-02-16Full publication date if available
- Authors
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Julian Zimmermann, Fabien Beguet, Daniel Guthruf, Bruno Langbehn, Daniela RuppList of authors in order
- Landing page
-
https://arxiv.org/abs/2208.11752Publisher landing page
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https://arxiv.org/pdf/2208.11752Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2208.11752Direct OA link when available
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Diffraction, Computer science, Artificial intelligence, Projection (relational algebra), Context (archaeology), Dimensionality reduction, Pattern recognition (psychology), Natural language processing, Optics, Physics, Algorithm, Geography, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.semantic | 79, 116 |
| abstract_inverted_index.temporal | 21 |
| abstract_inverted_index.yielding | 14 |
| abstract_inverted_index.analysis. | 70 |
| abstract_inverted_index.datasets, | 62 |
| abstract_inverted_index.intuition | 127 |
| abstract_inverted_index.labeling. | 90 |
| abstract_inverted_index.learning, | 97 |
| abstract_inverted_index.nanosized | 5 |
| abstract_inverted_index.particles | 6 |
| abstract_inverted_index.producing | 119 |
| abstract_inverted_index.real-time | 147 |
| abstract_inverted_index.recording | 35 |
| abstract_inverted_index.reduction | 118 |
| abstract_inverted_index.specimens | 49 |
| abstract_inverted_index.structure | 46 |
| abstract_inverted_index.containing | 37 |
| abstract_inverted_index.embeddings | 121 |
| abstract_inverted_index.meaningful | 120 |
| abstract_inverted_index.monumental | 66 |
| abstract_inverted_index.projection | 96 |
| abstract_inverted_index.remarkable | 9 |
| abstract_inverted_index.represents | 64 |
| abstract_inverted_index.structural | 52 |
| abstract_inverted_index.ultra-high | 18 |
| abstract_inverted_index.Single-shot | 0 |
| abstract_inverted_index.approaches, | 142 |
| abstract_inverted_index.automatized | 75 |
| abstract_inverted_index.contrastive | 101 |
| abstract_inverted_index.diffraction | 1, 40, 83, 108, 153 |
| abstract_inverted_index.experienced | 130 |
| abstract_inverted_index.experiments | 154 |
| abstract_inverted_index.improvement | 138 |
| abstract_inverted_index.introducing | 92 |
| abstract_inverted_index.large-scale | 149 |
| abstract_inverted_index.researcher. | 132 |
| abstract_inverted_index.resolution. | 22 |
| abstract_inverted_index.substantial | 137 |
| abstract_inverted_index.experiments. | 57 |
| abstract_inverted_index.measurements | 16 |
| abstract_inverted_index.similarities | 80 |
| abstract_inverted_index.determination | 47 |
| abstract_inverted_index.free-electron | 157 |
| abstract_inverted_index.dimensionality | 117 |
| abstract_inverted_index.self-supervised | 100 |
| abstract_inverted_index.high-repetition-rate | 26 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.00150288 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |