RadEdit: stress-testing biomedical vision models via diffusion image editing Article Swipe
Fernando Pérez‐García
,
Sam Bond-Taylor
,
Pedro P. Sanchez
,
Boris van Breugel
,
Daniel C. Castro
,
Harshita Sharma
,
Valentina Salvatelli
,
Maria Teodora Wetscherek
,
Hannah Richardson
,
Matthew P. Lungren
,
Aditya Nori
,
Javier Alvarez-Valle
,
Ozan Oktay
,
Maximilian Ilse
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.12865
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.12865
Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing. This work proposes using generative image editing to simulate dataset shifts and diagnose failure modes of biomedical vision models; this can be used in advance of deployment to assess readiness, potentially reducing cost and patient harm. Existing editing methods can produce undesirable changes, with spurious correlations learned due to the co-occurrence of disease and treatment interventions, limiting practical applicability. To address this, we train a text-to-image diffusion model on multiple chest X-ray datasets and introduce a new editing method RadEdit that uses multiple masks, if present, to constrain changes and ensure consistency in the edited images. We consider three types of dataset shifts: acquisition shift, manifestation shift, and population shift, and demonstrate that our approach can diagnose failures and quantify model robustness without additional data collection, complementing more qualitative tools for explainable AI.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.12865
- https://arxiv.org/pdf/2312.12865
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390092129
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390092129Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2312.12865Digital Object Identifier
- Title
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RadEdit: stress-testing biomedical vision models via diffusion image editingWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-20Full publication date if available
- Authors
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Fernando Pérez‐García, Sam Bond-Taylor, Pedro P. Sanchez, Boris van Breugel, Daniel C. Castro, Harshita Sharma, Valentina Salvatelli, Maria Teodora Wetscherek, Hannah Richardson, Matthew P. Lungren, Aditya Nori, Javier Alvarez-Valle, Ozan Oktay, Maximilian IlseList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.12865Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2312.12865Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2312.12865Direct OA link when available
- Concepts
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Spurious relationship, Computer science, Robustness (evolution), Artificial intelligence, Generative grammar, Software deployment, Stress testing (software), Machine learning, Software engineering, Programming language, Chemistry, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.models | 14 |
| abstract_inverted_index.shift, | 128, 130, 133 |
| abstract_inverted_index.shifts | 34 |
| abstract_inverted_index.vision | 41 |
| abstract_inverted_index.RadEdit | 103 |
| abstract_inverted_index.address | 84 |
| abstract_inverted_index.advance | 48 |
| abstract_inverted_index.biased, | 7 |
| abstract_inverted_index.changes | 112 |
| abstract_inverted_index.dataset | 33, 125 |
| abstract_inverted_index.disease | 76 |
| abstract_inverted_index.editing | 30, 61, 101 |
| abstract_inverted_index.failure | 37 |
| abstract_inverted_index.images. | 119 |
| abstract_inverted_index.imaging | 1 |
| abstract_inverted_index.learned | 70 |
| abstract_inverted_index.meaning | 8 |
| abstract_inverted_index.methods | 62 |
| abstract_inverted_index.models; | 42 |
| abstract_inverted_index.patient | 58 |
| abstract_inverted_index.produce | 64 |
| abstract_inverted_index.shifts: | 126 |
| abstract_inverted_index.without | 146 |
| abstract_inverted_index.Existing | 60 |
| abstract_inverted_index.approach | 138 |
| abstract_inverted_index.changes, | 66 |
| abstract_inverted_index.consider | 121 |
| abstract_inverted_index.datasets | 2, 96 |
| abstract_inverted_index.diagnose | 36, 140 |
| abstract_inverted_index.expected | 20 |
| abstract_inverted_index.failures | 141 |
| abstract_inverted_index.internal | 22 |
| abstract_inverted_index.limiting | 80 |
| abstract_inverted_index.multiple | 93, 106 |
| abstract_inverted_index.present, | 109 |
| abstract_inverted_index.proposes | 26 |
| abstract_inverted_index.quantify | 143 |
| abstract_inverted_index.reducing | 55 |
| abstract_inverted_index.simulate | 32 |
| abstract_inverted_index.spurious | 68 |
| abstract_inverted_index.testing. | 23 |
| abstract_inverted_index.constrain | 111 |
| abstract_inverted_index.diffusion | 90 |
| abstract_inverted_index.introduce | 98 |
| abstract_inverted_index.practical | 81 |
| abstract_inverted_index.treatment | 78 |
| abstract_inverted_index.Biomedical | 0 |
| abstract_inverted_index.additional | 147 |
| abstract_inverted_index.biomedical | 40 |
| abstract_inverted_index.deployment | 50 |
| abstract_inverted_index.generative | 28 |
| abstract_inverted_index.population | 132 |
| abstract_inverted_index.predictive | 13 |
| abstract_inverted_index.readiness, | 53 |
| abstract_inverted_index.real-world | 10 |
| abstract_inverted_index.robustness | 145 |
| abstract_inverted_index.acquisition | 127 |
| abstract_inverted_index.collection, | 149 |
| abstract_inverted_index.consistency | 115 |
| abstract_inverted_index.demonstrate | 135 |
| abstract_inverted_index.explainable | 155 |
| abstract_inverted_index.performance | 11 |
| abstract_inverted_index.potentially | 54 |
| abstract_inverted_index.qualitative | 152 |
| abstract_inverted_index.undesirable | 65 |
| abstract_inverted_index.correlations | 69 |
| abstract_inverted_index.co-occurrence | 74 |
| abstract_inverted_index.complementing | 150 |
| abstract_inverted_index.manifestation | 129 |
| abstract_inverted_index.substantially | 17 |
| abstract_inverted_index.text-to-image | 89 |
| abstract_inverted_index.applicability. | 82 |
| abstract_inverted_index.interventions, | 79 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 14 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.4000000059604645 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.78829301 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |