Using Variational Autoencoders for Out of Distribution Detection in Histological Multiple Instance Learning Article Swipe
Francisco Javier Sáez-Maldonado
,
Luz García
,
Lee Cooper
,
Jeffrey A. Goldstein
,
Rafael Molina
,
Aggelos K. Katsaggelos
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3593420
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3593420
In the context of histological image classification, Multiple Instance Learning (mil) methods only require labels at Whole Slide Image (wsi) level, effectively reducing the annotation bottleneck. However, for their deployment in real scenarios, they must be able to detect the presence of previously unseen tissues or artifacts, the so-called Out-of-Distribution (ood) samples. This would allow Computer Assisted Diagnosis systems to flag samples for additional quality or content control. In this work, we propose an ood-aware probabilistic deep mil model that combines the latent representation from a variational autoencoder and an attention mechanism. At test time, the latent representations of the instances are used in the classification and ood detection tasks. We also propose a deterministic version of the model that uses the reconstruction error as ood score. Panda (prostate tissue) and Camelyon16 (lymph node tissue) are used as train/test in-distribution datasets, obtaining bag classification results competitive with current state-of-the-art models. ood detection is evaluated performing two experiments for each in-distribution dataset. For Panda, Camelyon16 and artif (prostate tissue contaminated with artifacts) are used as ood datasets, obtaining 100% auc in both cases. For Camelyon16, Panda and bcell (lymph node tissue diagnosed with diffuse large B-cell lymphoma) are used as ood datasets, obtaining aucs of 100% and 97%, respectively. Experimental validation demonstrates the models' strong classification performance and effective ood slide detection, highlighting their clinical potential.
Related Topics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2025.3593420
- OA Status
- gold
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412722182
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4412722182Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2025.3593420Digital Object Identifier
- Title
-
Using Variational Autoencoders for Out of Distribution Detection in Histological Multiple Instance LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-01Full publication date if available
- Authors
-
Francisco Javier Sáez-Maldonado, Luz García, Lee Cooper, Jeffrey A. Goldstein, Rafael Molina, Aggelos K. KatsaggelosList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2025.3593420Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1109/access.2025.3593420Direct OA link when available
- Concepts
-
Artificial intelligence, Computer science, Pattern recognition (psychology)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
60Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4412722182 |
|---|---|
| doi | https://doi.org/10.1109/access.2025.3593420 |
| ids.doi | https://doi.org/10.1109/access.2025.3593420 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/41098231 |
| ids.openalex | https://openalex.org/W4412722182 |
| fwci | 0.0 |
| type | article |
| title | Using Variational Autoencoders for Out of Distribution Detection in Histological Multiple Instance Learning |
| biblio.issue | |
| biblio.volume | 13 |
| biblio.last_page | 133369 |
| biblio.first_page | 133351 |
| topics[0].id | https://openalex.org/T10862 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9994999766349792 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | AI in cancer detection |
| topics[1].id | https://openalex.org/T12874 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9868000149726868 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Digital Imaging for Blood Diseases |
| topics[2].id | https://openalex.org/T12422 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9775999784469604 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2741 |
| topics[2].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[2].display_name | Radiomics and Machine Learning in Medical Imaging |
| is_xpac | False |
| apc_list.value | 1850 |
| apc_list.currency | USD |
| apc_list.value_usd | 1850 |
| apc_paid.value | 1850 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 1850 |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.6617414355278015 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6564717888832092 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C153180895 |
| concepts[2].level | 2 |
| concepts[2].score | 0.526726484298706 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[2].display_name | Pattern recognition (psychology) |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.6617414355278015 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6564717888832092 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/pattern-recognition |
| keywords[2].score | 0.526726484298706 |
| keywords[2].display_name | Pattern recognition (psychology) |
| language | en |
| locations[0].id | doi:10.1109/access.2025.3593420 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2485537415 |
| locations[0].source.issn | 2169-3536 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2169-3536 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | IEEE Access |
| locations[0].source.host_organization | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_name | Institute of Electrical and Electronics Engineers |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | IEEE Access |
| locations[0].landing_page_url | https://doi.org/10.1109/access.2025.3593420 |
| locations[1].id | pmid:41098231 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | IEEE access : practical innovations, open solutions |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/41098231 |
| locations[2].id | pmh:oai:doaj.org/article:d59532d3f27648c7acac265ee56ae2d6 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | IEEE Access, Vol 13, Pp 133351-133369 (2025) |
| locations[2].landing_page_url | https://doaj.org/article/d59532d3f27648c7acac265ee56ae2d6 |
| locations[3].id | pmh:oai:europepmc.org:11328192 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306400806 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | Europe PMC (PubMed Central) |
| locations[3].source.host_organization | https://openalex.org/I1303153112 |
| locations[3].source.host_organization_name | European Bioinformatics Institute |
| locations[3].source.host_organization_lineage | https://openalex.org/I1303153112 |
| locations[3].license | cc-by |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/cc-by |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/12520607 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5119102192 |
| authorships[0].author.orcid | https://orcid.org/0009-0003-7838-5879 |
| authorships[0].author.display_name | Francisco Javier Sáez-Maldonado |
| authorships[0].countries | ES |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I173304897 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada, Spain |
| authorships[0].institutions[0].id | https://openalex.org/I173304897 |
| authorships[0].institutions[0].ror | https://ror.org/04njjy449 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I173304897 |
| authorships[0].institutions[0].country_code | ES |
| authorships[0].institutions[0].display_name | Universidad de Granada |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Francisco Javier Sáez-Maldonado |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada, Spain |
| authorships[1].author.id | https://openalex.org/A5013746163 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-5904-5412 |
| authorships[1].author.display_name | Luz García |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Signal Theory, Telematics and Communications, Granada, Spain |
| authorships[1].affiliations[1].raw_affiliation_string | Department of Signal Theory, Telematics, and Communications, Granada, Spain |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Luz García |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Signal Theory, Telematics and Communications, Granada, Spain, Department of Signal Theory, Telematics, and Communications, Granada, Spain |
| authorships[2].author.id | https://openalex.org/A5034820084 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-3504-4965 |
| authorships[2].author.display_name | Lee Cooper |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I111979921 |
| authorships[2].affiliations[0].raw_affiliation_string | Center for Computational Imaging and Signal Analytics, Northwestern University, Chicago, IL, USA |
| authorships[2].institutions[0].id | https://openalex.org/I111979921 |
| authorships[2].institutions[0].ror | https://ror.org/000e0be47 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I111979921 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Northwestern University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Lee A. D. Cooper |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Center for Computational Imaging and Signal Analytics, Northwestern University, Chicago, IL, USA |
| authorships[3].author.id | https://openalex.org/A5088880350 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-4086-057X |
| authorships[3].author.display_name | Jeffrey A. Goldstein |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I111979921 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Pathology, Northwestern University, Chicago, IL, USA |
| authorships[3].institutions[0].id | https://openalex.org/I111979921 |
| authorships[3].institutions[0].ror | https://ror.org/000e0be47 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I111979921 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Northwestern University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Jeffery A. Goldstein |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Pathology, Northwestern University, Chicago, IL, USA |
| authorships[4].author.id | https://openalex.org/A5023830568 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-4694-8588 |
| authorships[4].author.display_name | Rafael Molina |
| authorships[4].countries | ES |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I173304897 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada, Spain |
| authorships[4].institutions[0].id | https://openalex.org/I173304897 |
| authorships[4].institutions[0].ror | https://ror.org/04njjy449 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I173304897 |
| authorships[4].institutions[0].country_code | ES |
| authorships[4].institutions[0].display_name | Universidad de Granada |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Rafael Molina |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Computer Science and Artificial Intelligence, Universidad de Granada, Granada, Spain |
| authorships[5].author.id | https://openalex.org/A5048650003 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-4554-0070 |
| authorships[5].author.display_name | Aggelos K. Katsaggelos |
| authorships[5].countries | US |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I111979921 |
| authorships[5].affiliations[0].raw_affiliation_string | Center for Computational Imaging and Signal Analytics, Northwestern University, Chicago, IL, USA |
| authorships[5].institutions[0].id | https://openalex.org/I111979921 |
| authorships[5].institutions[0].ror | https://ror.org/000e0be47 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I111979921 |
| authorships[5].institutions[0].country_code | US |
| authorships[5].institutions[0].display_name | Northwestern University |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Aggelos K. Katsaggelos |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Center for Computational Imaging and Signal Analytics, Northwestern University, Chicago, IL, USA |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1109/access.2025.3593420 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Using Variational Autoencoders for Out of Distribution Detection in Histological Multiple Instance Learning |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10862 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9994999766349792 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | AI in cancer detection |
| related_works | https://openalex.org/W4391375266, https://openalex.org/W2899084033, https://openalex.org/W2748952813, https://openalex.org/W2390279801, https://openalex.org/W4391913857, https://openalex.org/W2358668433, https://openalex.org/W4396701345, https://openalex.org/W2376932109, https://openalex.org/W2033914206, https://openalex.org/W2042327336 |
| cited_by_count | 0 |
| locations_count | 4 |
| best_oa_location.id | doi:10.1109/access.2025.3593420 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2485537415 |
| best_oa_location.source.issn | 2169-3536 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2169-3536 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | IEEE Access |
| best_oa_location.source.host_organization | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | IEEE Access |
| best_oa_location.landing_page_url | https://doi.org/10.1109/access.2025.3593420 |
| primary_location.id | doi:10.1109/access.2025.3593420 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2485537415 |
| primary_location.source.issn | 2169-3536 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2169-3536 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | IEEE Access |
| primary_location.source.host_organization | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | IEEE Access |
| primary_location.landing_page_url | https://doi.org/10.1109/access.2025.3593420 |
| publication_date | 2025-01-01 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4390841990, https://openalex.org/W4394566797, https://openalex.org/W6683033130, https://openalex.org/W2125479168, https://openalex.org/W4237301547, https://openalex.org/W6857859486, https://openalex.org/W2956228567, https://openalex.org/W2010792435, https://openalex.org/W6851987887, https://openalex.org/W2531897166, https://openalex.org/W6747701563, https://openalex.org/W6803426362, https://openalex.org/W4296122973, https://openalex.org/W3176719058, https://openalex.org/W4312765357, https://openalex.org/W4403894869, https://openalex.org/W4387211727, https://openalex.org/W4390639492, https://openalex.org/W1932198206, https://openalex.org/W3184778778, https://openalex.org/W4389990732, https://openalex.org/W3176446229, https://openalex.org/W4306648046, https://openalex.org/W3210506295, https://openalex.org/W6838380953, https://openalex.org/W3113089224, https://openalex.org/W2571156573, https://openalex.org/W4224941647, https://openalex.org/W3118929067, https://openalex.org/W4387098720, https://openalex.org/W4399357306, https://openalex.org/W6776560590, https://openalex.org/W2947805333, https://openalex.org/W4390880024, https://openalex.org/W4404200677, https://openalex.org/W3196691777, https://openalex.org/W2981591098, https://openalex.org/W3210805939, https://openalex.org/W4224979692, https://openalex.org/W6771822268, https://openalex.org/W4280645769, https://openalex.org/W3039412006, https://openalex.org/W6845860122, https://openalex.org/W6640963894, https://openalex.org/W4212863985, https://openalex.org/W6752145142, https://openalex.org/W2772723798, https://openalex.org/W4205900565, https://openalex.org/W6791742336, https://openalex.org/W4386076317, https://openalex.org/W4392947521, https://openalex.org/W3135547872, https://openalex.org/W2155653793, https://openalex.org/W6766978945, https://openalex.org/W6631190155, https://openalex.org/W2782223840, https://openalex.org/W1522301498, https://openalex.org/W1959608418, https://openalex.org/W4287995766, https://openalex.org/W4207066074 |
| referenced_works_count | 60 |
| abstract_inverted_index.a | 85, 113 |
| abstract_inverted_index.At | 92 |
| abstract_inverted_index.In | 0, 68 |
| abstract_inverted_index.We | 110 |
| abstract_inverted_index.an | 73, 89 |
| abstract_inverted_index.as | 124, 137, 173, 198 |
| abstract_inverted_index.at | 15 |
| abstract_inverted_index.be | 35 |
| abstract_inverted_index.in | 30, 103, 179 |
| abstract_inverted_index.is | 152 |
| abstract_inverted_index.of | 3, 41, 98, 116, 203 |
| abstract_inverted_index.or | 45, 65 |
| abstract_inverted_index.to | 37, 59 |
| abstract_inverted_index.we | 71 |
| abstract_inverted_index.For | 161, 182 |
| abstract_inverted_index.and | 88, 106, 130, 164, 185, 205, 216 |
| abstract_inverted_index.are | 101, 135, 171, 196 |
| abstract_inverted_index.auc | 178 |
| abstract_inverted_index.bag | 142 |
| abstract_inverted_index.for | 27, 62, 157 |
| abstract_inverted_index.mil | 77 |
| abstract_inverted_index.ood | 107, 125, 150, 174, 199, 218 |
| abstract_inverted_index.the | 1, 23, 39, 47, 81, 95, 99, 104, 117, 121, 211 |
| abstract_inverted_index.two | 155 |
| abstract_inverted_index.100% | 177, 204 |
| abstract_inverted_index.97%, | 206 |
| abstract_inverted_index.This | 52 |
| abstract_inverted_index.able | 36 |
| abstract_inverted_index.also | 111 |
| abstract_inverted_index.aucs | 202 |
| abstract_inverted_index.both | 180 |
| abstract_inverted_index.deep | 76 |
| abstract_inverted_index.each | 158 |
| abstract_inverted_index.flag | 60 |
| abstract_inverted_index.from | 84 |
| abstract_inverted_index.must | 34 |
| abstract_inverted_index.node | 133, 188 |
| abstract_inverted_index.only | 12 |
| abstract_inverted_index.real | 31 |
| abstract_inverted_index.test | 93 |
| abstract_inverted_index.that | 79, 119 |
| abstract_inverted_index.they | 33 |
| abstract_inverted_index.this | 69 |
| abstract_inverted_index.used | 102, 136, 172, 197 |
| abstract_inverted_index.uses | 120 |
| abstract_inverted_index.with | 146, 169, 191 |
| abstract_inverted_index.(mil) | 10 |
| abstract_inverted_index.(ood) | 50 |
| abstract_inverted_index.(wsi) | 19 |
| abstract_inverted_index.Image | 18 |
| abstract_inverted_index.Panda | 127, 184 |
| abstract_inverted_index.Slide | 17 |
| abstract_inverted_index.Whole | 16 |
| abstract_inverted_index.allow | 54 |
| abstract_inverted_index.artif | 165 |
| abstract_inverted_index.bcell | 186 |
| abstract_inverted_index.error | 123 |
| abstract_inverted_index.image | 5 |
| abstract_inverted_index.large | 193 |
| abstract_inverted_index.model | 78, 118 |
| abstract_inverted_index.slide | 219 |
| abstract_inverted_index.their | 28, 222 |
| abstract_inverted_index.time, | 94 |
| abstract_inverted_index.work, | 70 |
| abstract_inverted_index.would | 53 |
| abstract_inverted_index.(lymph | 132, 187 |
| abstract_inverted_index.B-cell | 194 |
| abstract_inverted_index.Panda, | 162 |
| abstract_inverted_index.cases. | 181 |
| abstract_inverted_index.detect | 38 |
| abstract_inverted_index.labels | 14 |
| abstract_inverted_index.latent | 82, 96 |
| abstract_inverted_index.level, | 20 |
| abstract_inverted_index.score. | 126 |
| abstract_inverted_index.strong | 213 |
| abstract_inverted_index.tasks. | 109 |
| abstract_inverted_index.tissue | 167, 189 |
| abstract_inverted_index.unseen | 43 |
| abstract_inverted_index.content | 66 |
| abstract_inverted_index.context | 2 |
| abstract_inverted_index.current | 147 |
| abstract_inverted_index.diffuse | 192 |
| abstract_inverted_index.methods | 11 |
| abstract_inverted_index.models' | 212 |
| abstract_inverted_index.models. | 149 |
| abstract_inverted_index.propose | 72, 112 |
| abstract_inverted_index.quality | 64 |
| abstract_inverted_index.require | 13 |
| abstract_inverted_index.results | 144 |
| abstract_inverted_index.samples | 61 |
| abstract_inverted_index.systems | 58 |
| abstract_inverted_index.tissue) | 129, 134 |
| abstract_inverted_index.tissues | 44 |
| abstract_inverted_index.version | 115 |
| abstract_inverted_index.Assisted | 56 |
| abstract_inverted_index.Computer | 55 |
| abstract_inverted_index.However, | 26 |
| abstract_inverted_index.Instance | 8 |
| abstract_inverted_index.Learning | 9 |
| abstract_inverted_index.Multiple | 7 |
| abstract_inverted_index.clinical | 223 |
| abstract_inverted_index.combines | 80 |
| abstract_inverted_index.control. | 67 |
| abstract_inverted_index.dataset. | 160 |
| abstract_inverted_index.presence | 40 |
| abstract_inverted_index.reducing | 22 |
| abstract_inverted_index.samples. | 51 |
| abstract_inverted_index.(prostate | 128, 166 |
| abstract_inverted_index.Diagnosis | 57 |
| abstract_inverted_index.attention | 90 |
| abstract_inverted_index.datasets, | 140, 175, 200 |
| abstract_inverted_index.detection | 108, 151 |
| abstract_inverted_index.diagnosed | 190 |
| abstract_inverted_index.effective | 217 |
| abstract_inverted_index.evaluated | 153 |
| abstract_inverted_index.instances | 100 |
| abstract_inverted_index.lymphoma) | 195 |
| abstract_inverted_index.obtaining | 141, 176, 201 |
| abstract_inverted_index.ood-aware | 74 |
| abstract_inverted_index.so-called | 48 |
| abstract_inverted_index.Camelyon16 | 131, 163 |
| abstract_inverted_index.additional | 63 |
| abstract_inverted_index.annotation | 24 |
| abstract_inverted_index.artifacts) | 170 |
| abstract_inverted_index.artifacts, | 46 |
| abstract_inverted_index.deployment | 29 |
| abstract_inverted_index.detection, | 220 |
| abstract_inverted_index.mechanism. | 91 |
| abstract_inverted_index.performing | 154 |
| abstract_inverted_index.potential. | 224 |
| abstract_inverted_index.previously | 42 |
| abstract_inverted_index.scenarios, | 32 |
| abstract_inverted_index.train/test | 138 |
| abstract_inverted_index.validation | 209 |
| abstract_inverted_index.Camelyon16, | 183 |
| abstract_inverted_index.autoencoder | 87 |
| abstract_inverted_index.bottleneck. | 25 |
| abstract_inverted_index.competitive | 145 |
| abstract_inverted_index.effectively | 21 |
| abstract_inverted_index.experiments | 156 |
| abstract_inverted_index.performance | 215 |
| abstract_inverted_index.variational | 86 |
| abstract_inverted_index.Experimental | 208 |
| abstract_inverted_index.contaminated | 168 |
| abstract_inverted_index.demonstrates | 210 |
| abstract_inverted_index.highlighting | 221 |
| abstract_inverted_index.histological | 4 |
| abstract_inverted_index.deterministic | 114 |
| abstract_inverted_index.probabilistic | 75 |
| abstract_inverted_index.respectively. | 207 |
| abstract_inverted_index.classification | 105, 143, 214 |
| abstract_inverted_index.reconstruction | 122 |
| abstract_inverted_index.representation | 83 |
| abstract_inverted_index.classification, | 6 |
| abstract_inverted_index.in-distribution | 139, 159 |
| abstract_inverted_index.representations | 97 |
| abstract_inverted_index.state-of-the-art | 148 |
| abstract_inverted_index.Out-of-Distribution | 49 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.13584789 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |