Stable Diffusion Segmentation for Biomedical Images with Single-step Reverse Process Article Swipe
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.18361
Diffusion models have demonstrated their effectiveness across various generative tasks. However, when applied to medical image segmentation, these models encounter several challenges, including significant resource and time requirements. They also necessitate a multi-step reverse process and multiple samples to produce reliable predictions. To address these challenges, we introduce the first latent diffusion segmentation model, named SDSeg, built upon stable diffusion (SD). SDSeg incorporates a straightforward latent estimation strategy to facilitate a single-step reverse process and utilizes latent fusion concatenation to remove the necessity for multiple samples. Extensive experiments indicate that SDSeg surpasses existing state-of-the-art methods on five benchmark datasets featuring diverse imaging modalities. Remarkably, SDSeg is capable of generating stable predictions with a solitary reverse step and sample, epitomizing the model's stability as implied by its name. The code is available at https://github.com/lin-tianyu/Stable-Diffusion-Seg
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.18361
- https://arxiv.org/pdf/2406.18361
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400142299
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4400142299Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2406.18361Digital Object Identifier
- Title
-
Stable Diffusion Segmentation for Biomedical Images with Single-step Reverse ProcessWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-26Full publication date if available
- Authors
-
Tianyu Lin, Zhiguang Chen, Zhonghao Yan, Weijiang Yu, Fudan ZhengList of authors in order
- Landing page
-
https://arxiv.org/abs/2406.18361Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2406.18361Direct 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/2406.18361Direct OA link when available
- Concepts
-
Process (computing), Segmentation, Computer science, Artificial intelligence, Diffusion, Computer vision, Two step, Pattern recognition (psychology), Mathematics, Physics, Applied mathematics, Thermodynamics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4400142299 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2406.18361 |
| ids.doi | https://doi.org/10.48550/arxiv.2406.18361 |
| ids.openalex | https://openalex.org/W4400142299 |
| fwci | |
| type | preprint |
| title | Stable Diffusion Segmentation for Biomedical Images with Single-step Reverse Process |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10052 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9642999768257141 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Medical Image Segmentation Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C98045186 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6068627834320068 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q205663 |
| concepts[0].display_name | Process (computing) |
| concepts[1].id | https://openalex.org/C89600930 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5644513368606567 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[1].display_name | Segmentation |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.548976719379425 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5308486223220825 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C69357855 |
| concepts[4].level | 2 |
| concepts[4].score | 0.49279454350471497 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q163214 |
| concepts[4].display_name | Diffusion |
| concepts[5].id | https://openalex.org/C31972630 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4757370948791504 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[5].display_name | Computer vision |
| concepts[6].id | https://openalex.org/C3019136120 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4428546130657196 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2462428 |
| concepts[6].display_name | Two step |
| concepts[7].id | https://openalex.org/C153180895 |
| concepts[7].level | 2 |
| concepts[7].score | 0.33796700835227966 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[7].display_name | Pattern recognition (psychology) |
| concepts[8].id | https://openalex.org/C33923547 |
| concepts[8].level | 0 |
| concepts[8].score | 0.20259615778923035 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[8].display_name | Mathematics |
| concepts[9].id | https://openalex.org/C121332964 |
| concepts[9].level | 0 |
| concepts[9].score | 0.0741635262966156 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[9].display_name | Physics |
| concepts[10].id | https://openalex.org/C28826006 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q33521 |
| concepts[10].display_name | Applied mathematics |
| concepts[11].id | https://openalex.org/C97355855 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11473 |
| concepts[11].display_name | Thermodynamics |
| concepts[12].id | https://openalex.org/C111919701 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[12].display_name | Operating system |
| keywords[0].id | https://openalex.org/keywords/process |
| keywords[0].score | 0.6068627834320068 |
| keywords[0].display_name | Process (computing) |
| keywords[1].id | https://openalex.org/keywords/segmentation |
| keywords[1].score | 0.5644513368606567 |
| keywords[1].display_name | Segmentation |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.548976719379425 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.5308486223220825 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/diffusion |
| keywords[4].score | 0.49279454350471497 |
| keywords[4].display_name | Diffusion |
| keywords[5].id | https://openalex.org/keywords/computer-vision |
| keywords[5].score | 0.4757370948791504 |
| keywords[5].display_name | Computer vision |
| keywords[6].id | https://openalex.org/keywords/two-step |
| keywords[6].score | 0.4428546130657196 |
| keywords[6].display_name | Two step |
| keywords[7].id | https://openalex.org/keywords/pattern-recognition |
| keywords[7].score | 0.33796700835227966 |
| keywords[7].display_name | Pattern recognition (psychology) |
| keywords[8].id | https://openalex.org/keywords/mathematics |
| keywords[8].score | 0.20259615778923035 |
| keywords[8].display_name | Mathematics |
| keywords[9].id | https://openalex.org/keywords/physics |
| keywords[9].score | 0.0741635262966156 |
| keywords[9].display_name | Physics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2406.18361 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2406.18361 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2406.18361 |
| locations[1].id | doi:10.48550/arxiv.2406.18361 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2406.18361 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5040959432 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Tianyu Lin |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Lin, Tianyu |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5101483479 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-9318-5715 |
| authorships[1].author.display_name | Zhiguang Chen |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Chen, Zhiguang |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5100312997 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Zhonghao Yan |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Yan, Zhonghao |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5055989750 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-7449-3093 |
| authorships[3].author.display_name | Weijiang Yu |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Yu, Weijiang |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5024126710 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-9664-012X |
| authorships[4].author.display_name | Fudan Zheng |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Zheng, Fudan |
| authorships[4].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2406.18361 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2024-06-29T00:00:00 |
| display_name | Stable Diffusion Segmentation for Biomedical Images with Single-step Reverse Process |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10052 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9642999768257141 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Medical Image Segmentation Techniques |
| related_works | https://openalex.org/W2186126999, https://openalex.org/W2996157879, https://openalex.org/W1646940918, https://openalex.org/W2805196090, https://openalex.org/W2894912768, https://openalex.org/W4379231730, https://openalex.org/W4389858081, https://openalex.org/W2501551404, https://openalex.org/W4385583601, https://openalex.org/W4298131179 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2406.18361 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2406.18361 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2406.18361 |
| primary_location.id | pmh:oai:arXiv.org:2406.18361 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2406.18361 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2406.18361 |
| publication_date | 2024-06-26 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 31, 63, 70, 112 |
| abstract_inverted_index.To | 42 |
| abstract_inverted_index.as | 122 |
| abstract_inverted_index.at | 131 |
| abstract_inverted_index.by | 124 |
| abstract_inverted_index.is | 105, 129 |
| abstract_inverted_index.of | 107 |
| abstract_inverted_index.on | 95 |
| abstract_inverted_index.to | 13, 38, 68, 79 |
| abstract_inverted_index.we | 46 |
| abstract_inverted_index.The | 127 |
| abstract_inverted_index.and | 25, 35, 74, 116 |
| abstract_inverted_index.for | 83 |
| abstract_inverted_index.its | 125 |
| abstract_inverted_index.the | 48, 81, 119 |
| abstract_inverted_index.They | 28 |
| abstract_inverted_index.also | 29 |
| abstract_inverted_index.code | 128 |
| abstract_inverted_index.five | 96 |
| abstract_inverted_index.have | 2 |
| abstract_inverted_index.step | 115 |
| abstract_inverted_index.that | 89 |
| abstract_inverted_index.time | 26 |
| abstract_inverted_index.upon | 57 |
| abstract_inverted_index.when | 11 |
| abstract_inverted_index.with | 111 |
| abstract_inverted_index.(SD). | 60 |
| abstract_inverted_index.SDSeg | 61, 90, 104 |
| abstract_inverted_index.built | 56 |
| abstract_inverted_index.first | 49 |
| abstract_inverted_index.image | 15 |
| abstract_inverted_index.name. | 126 |
| abstract_inverted_index.named | 54 |
| abstract_inverted_index.their | 4 |
| abstract_inverted_index.these | 17, 44 |
| abstract_inverted_index.SDSeg, | 55 |
| abstract_inverted_index.across | 6 |
| abstract_inverted_index.fusion | 77 |
| abstract_inverted_index.latent | 50, 65, 76 |
| abstract_inverted_index.model, | 53 |
| abstract_inverted_index.models | 1, 18 |
| abstract_inverted_index.remove | 80 |
| abstract_inverted_index.stable | 58, 109 |
| abstract_inverted_index.tasks. | 9 |
| abstract_inverted_index.address | 43 |
| abstract_inverted_index.applied | 12 |
| abstract_inverted_index.capable | 106 |
| abstract_inverted_index.diverse | 100 |
| abstract_inverted_index.imaging | 101 |
| abstract_inverted_index.implied | 123 |
| abstract_inverted_index.medical | 14 |
| abstract_inverted_index.methods | 94 |
| abstract_inverted_index.model's | 120 |
| abstract_inverted_index.process | 34, 73 |
| abstract_inverted_index.produce | 39 |
| abstract_inverted_index.reverse | 33, 72, 114 |
| abstract_inverted_index.sample, | 117 |
| abstract_inverted_index.samples | 37 |
| abstract_inverted_index.several | 20 |
| abstract_inverted_index.various | 7 |
| abstract_inverted_index.However, | 10 |
| abstract_inverted_index.datasets | 98 |
| abstract_inverted_index.existing | 92 |
| abstract_inverted_index.indicate | 88 |
| abstract_inverted_index.multiple | 36, 84 |
| abstract_inverted_index.reliable | 40 |
| abstract_inverted_index.resource | 24 |
| abstract_inverted_index.samples. | 85 |
| abstract_inverted_index.solitary | 113 |
| abstract_inverted_index.strategy | 67 |
| abstract_inverted_index.utilizes | 75 |
| abstract_inverted_index.Diffusion | 0 |
| abstract_inverted_index.Extensive | 86 |
| abstract_inverted_index.available | 130 |
| abstract_inverted_index.benchmark | 97 |
| abstract_inverted_index.diffusion | 51, 59 |
| abstract_inverted_index.encounter | 19 |
| abstract_inverted_index.featuring | 99 |
| abstract_inverted_index.including | 22 |
| abstract_inverted_index.introduce | 47 |
| abstract_inverted_index.necessity | 82 |
| abstract_inverted_index.stability | 121 |
| abstract_inverted_index.surpasses | 91 |
| abstract_inverted_index.estimation | 66 |
| abstract_inverted_index.facilitate | 69 |
| abstract_inverted_index.generating | 108 |
| abstract_inverted_index.generative | 8 |
| abstract_inverted_index.multi-step | 32 |
| abstract_inverted_index.Remarkably, | 103 |
| abstract_inverted_index.challenges, | 21, 45 |
| abstract_inverted_index.epitomizing | 118 |
| abstract_inverted_index.experiments | 87 |
| abstract_inverted_index.modalities. | 102 |
| abstract_inverted_index.necessitate | 30 |
| abstract_inverted_index.predictions | 110 |
| abstract_inverted_index.significant | 23 |
| abstract_inverted_index.single-step | 71 |
| abstract_inverted_index.demonstrated | 3 |
| abstract_inverted_index.incorporates | 62 |
| abstract_inverted_index.predictions. | 41 |
| abstract_inverted_index.segmentation | 52 |
| abstract_inverted_index.concatenation | 78 |
| abstract_inverted_index.effectiveness | 5 |
| abstract_inverted_index.requirements. | 27 |
| abstract_inverted_index.segmentation, | 16 |
| abstract_inverted_index.straightforward | 64 |
| abstract_inverted_index.state-of-the-art | 93 |
| abstract_inverted_index.https://github.com/lin-tianyu/Stable-Diffusion-Seg | 132 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |