EMTL-Net: Boosting segmentation quality in histopathology images of gland and nuclei by explainable multitask learning network as an optimized strategy Article Swipe
Haider Ali
,
Mingzhao Wang
,
Juanying Xie
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jestch.2024.101636
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jestch.2024.101636
In spite of achieving human-level efficacy in gland and nuclei segmentation, modern deep learning-driven techniques still face challenges related to the loss of regional context information and disregard the importance of long-range semantic information during the optimization. Furthermore, one typical issue with CNN-based approaches is also their lack of clinical explainability. In such context, we propose an explainable multitask learning network (EMTL-Net) to enhance the segmentation quality of gland and nuclei segmentation via histopathology images. Initially, to obtain better feature maps, boost the network growth, and reduce the computational cost in the encoding path, we introduce the tweaked residual module (TRM) in which the diagnosis information of histopathology images structure will not be overlooked. After that, we design the two modules to address the challenges of regional context information, such as the complementary feature detection module (CFDM) for the intermediate layers and the cascade feature module (CFM) for the deeper layer of the encoder block. We also design a competitive decoder by casting an efficient pyramid split attention (EPSA) unit that preserves long-range semantic information, stimulates robust features, and dynamically recalibrates spatial and channel-wise features. Extensive experimental findings on real-world datasets, including Glas, MoNuSeg and TNBC, reveal that our proposed model has achieved a Dice Coefficient and Intersection over Union of 0.923 and 0.869 on Glas, 0.855 and 0.741 on TNBC, and 0.829 and 0.713 on MoNuSeg when compared with the SOTA approaches for gland and nuclei segmentation. In addition, the suggested EMTL-Net is also examined for paying full attention on the regions of the gland and nuclei to meet the needs of clinical scenarios.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jestch.2024.101636
- OA Status
- diamond
- Cited By
- 7
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391920951
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4391920951Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.jestch.2024.101636Digital Object Identifier
- Title
-
EMTL-Net: Boosting segmentation quality in histopathology images of gland and nuclei by explainable multitask learning network as an optimized strategyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-09Full publication date if available
- Authors
-
Haider Ali, Mingzhao Wang, Juanying XieList of authors in order
- Landing page
-
https://doi.org/10.1016/j.jestch.2024.101636Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.jestch.2024.101636Direct OA link when available
- Concepts
-
Boosting (machine learning), Histopathology, Segmentation, Artificial intelligence, Computer science, Pattern recognition (psychology), Multi-task learning, Machine learning, Medicine, Pathology, Engineering, Task (project management), Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
63Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4391920951 |
|---|---|
| doi | https://doi.org/10.1016/j.jestch.2024.101636 |
| ids.doi | https://doi.org/10.1016/j.jestch.2024.101636 |
| ids.openalex | https://openalex.org/W4391920951 |
| fwci | 4.47144984 |
| type | article |
| title | EMTL-Net: Boosting segmentation quality in histopathology images of gland and nuclei by explainable multitask learning network as an optimized strategy |
| awards[0].id | https://openalex.org/G8686470721 |
| awards[0].funder_id | https://openalex.org/F4320321001 |
| awards[0].display_name | |
| awards[0].funder_award_id | 61673251 |
| awards[0].funder_display_name | National Natural Science Foundation of China |
| awards[1].id | https://openalex.org/G8651971177 |
| awards[1].funder_id | https://openalex.org/F4320335787 |
| awards[1].display_name | |
| awards[1].funder_award_id | GK202105003 |
| awards[1].funder_display_name | Fundamental Research Funds for the Central Universities |
| awards[2].id | https://openalex.org/G148668811 |
| awards[2].funder_id | https://openalex.org/F4320321001 |
| awards[2].display_name | |
| awards[2].funder_award_id | 12031010 |
| awards[2].funder_display_name | National Natural Science Foundation of China |
| awards[3].id | https://openalex.org/G6872535895 |
| awards[3].funder_id | https://openalex.org/F4320335787 |
| awards[3].display_name | |
| awards[3].funder_award_id | GK202304044 |
| awards[3].funder_display_name | Fundamental Research Funds for the Central Universities |
| awards[4].id | https://openalex.org/G166695096 |
| awards[4].funder_id | https://openalex.org/F4320321001 |
| awards[4].display_name | |
| awards[4].funder_award_id | 62076159 |
| awards[4].funder_display_name | National Natural Science Foundation of China |
| awards[5].id | https://openalex.org/G5390142772 |
| awards[5].funder_id | https://openalex.org/F4320335787 |
| awards[5].display_name | |
| awards[5].funder_award_id | GK202207017 |
| awards[5].funder_display_name | Fundamental Research Funds for the Central Universities |
| biblio.issue | |
| biblio.volume | 51 |
| biblio.last_page | 101636 |
| biblio.first_page | 101636 |
| 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/T14510 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9894000291824341 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2204 |
| topics[1].subfield.display_name | Biomedical Engineering |
| topics[1].display_name | Medical Imaging and Analysis |
| 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 | 0.9871000051498413 |
| 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 |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| funders[1].id | https://openalex.org/F4320326174 |
| funders[1].ror | |
| funders[1].display_name | Shaanxi Province Postdoctoral Science Foundation |
| funders[2].id | https://openalex.org/F4320328119 |
| funders[2].ror | |
| funders[2].display_name | National University's Basic Research Foundation of China |
| funders[3].id | https://openalex.org/F4320335787 |
| funders[3].ror | |
| funders[3].display_name | Fundamental Research Funds for the Central Universities |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C46686674 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8324148654937744 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q466303 |
| concepts[0].display_name | Boosting (machine learning) |
| concepts[1].id | https://openalex.org/C544855455 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6628128886222839 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1070952 |
| concepts[1].display_name | Histopathology |
| concepts[2].id | https://openalex.org/C89600930 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6358186602592468 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[2].display_name | Segmentation |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5993055701255798 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.5092865228652954 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C153180895 |
| concepts[5].level | 2 |
| concepts[5].score | 0.48768293857574463 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[5].display_name | Pattern recognition (psychology) |
| concepts[6].id | https://openalex.org/C28006648 |
| concepts[6].level | 3 |
| concepts[6].score | 0.4337661862373352 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q6934509 |
| concepts[6].display_name | Multi-task learning |
| concepts[7].id | https://openalex.org/C119857082 |
| concepts[7].level | 1 |
| concepts[7].score | 0.4081585705280304 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[7].display_name | Machine learning |
| concepts[8].id | https://openalex.org/C71924100 |
| concepts[8].level | 0 |
| concepts[8].score | 0.2511681318283081 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[8].display_name | Medicine |
| concepts[9].id | https://openalex.org/C142724271 |
| concepts[9].level | 1 |
| concepts[9].score | 0.17754557728767395 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7208 |
| concepts[9].display_name | Pathology |
| concepts[10].id | https://openalex.org/C127413603 |
| concepts[10].level | 0 |
| concepts[10].score | 0.12061062455177307 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[10].display_name | Engineering |
| concepts[11].id | https://openalex.org/C2780451532 |
| concepts[11].level | 2 |
| concepts[11].score | 0.07060515880584717 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q759676 |
| concepts[11].display_name | Task (project management) |
| concepts[12].id | https://openalex.org/C201995342 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q682496 |
| concepts[12].display_name | Systems engineering |
| keywords[0].id | https://openalex.org/keywords/boosting |
| keywords[0].score | 0.8324148654937744 |
| keywords[0].display_name | Boosting (machine learning) |
| keywords[1].id | https://openalex.org/keywords/histopathology |
| keywords[1].score | 0.6628128886222839 |
| keywords[1].display_name | Histopathology |
| keywords[2].id | https://openalex.org/keywords/segmentation |
| keywords[2].score | 0.6358186602592468 |
| keywords[2].display_name | Segmentation |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.5993055701255798 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.5092865228652954 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/pattern-recognition |
| keywords[5].score | 0.48768293857574463 |
| keywords[5].display_name | Pattern recognition (psychology) |
| keywords[6].id | https://openalex.org/keywords/multi-task-learning |
| keywords[6].score | 0.4337661862373352 |
| keywords[6].display_name | Multi-task learning |
| keywords[7].id | https://openalex.org/keywords/machine-learning |
| keywords[7].score | 0.4081585705280304 |
| keywords[7].display_name | Machine learning |
| keywords[8].id | https://openalex.org/keywords/medicine |
| keywords[8].score | 0.2511681318283081 |
| keywords[8].display_name | Medicine |
| keywords[9].id | https://openalex.org/keywords/pathology |
| keywords[9].score | 0.17754557728767395 |
| keywords[9].display_name | Pathology |
| keywords[10].id | https://openalex.org/keywords/engineering |
| keywords[10].score | 0.12061062455177307 |
| keywords[10].display_name | Engineering |
| keywords[11].id | https://openalex.org/keywords/task |
| keywords[11].score | 0.07060515880584717 |
| keywords[11].display_name | Task (project management) |
| language | en |
| locations[0].id | doi:10.1016/j.jestch.2024.101636 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2764510487 |
| locations[0].source.issn | 2215-0986 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2215-0986 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Engineering Science and Technology an International Journal |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Engineering Science and Technology, an International Journal |
| locations[0].landing_page_url | https://doi.org/10.1016/j.jestch.2024.101636 |
| locations[1].id | pmh:oai:doaj.org/article:42fd19a35ac94e46a1d2f5b12e65b0da |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| 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 | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Engineering Science and Technology, an International Journal, Vol 51, Iss , Pp 101636- (2024) |
| locations[1].landing_page_url | https://doaj.org/article/42fd19a35ac94e46a1d2f5b12e65b0da |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5101668195 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-2064-5079 |
| authorships[0].author.display_name | Haider Ali |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Haider Ali |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5087991546 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-8277-4963 |
| authorships[1].author.display_name | Mingzhao Wang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Mingzhao Wang |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5076397429 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-6540-4397 |
| authorships[2].author.display_name | Juanying Xie |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Juanying Xie |
| authorships[2].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://doi.org/10.1016/j.jestch.2024.101636 |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | EMTL-Net: Boosting segmentation quality in histopathology images of gland and nuclei by explainable multitask learning network as an optimized strategy |
| 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/W3039419443, https://openalex.org/W2125652721, https://openalex.org/W4386772532, https://openalex.org/W1540371141, https://openalex.org/W4393212117, https://openalex.org/W1549363203, https://openalex.org/W2147697413, https://openalex.org/W3082059448, https://openalex.org/W4313640622, https://openalex.org/W4390735358 |
| cited_by_count | 7 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 5 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 2 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1016/j.jestch.2024.101636 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764510487 |
| best_oa_location.source.issn | 2215-0986 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2215-0986 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Engineering Science and Technology an International Journal |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_lineage_names | Elsevier BV |
| best_oa_location.license | cc-by-nc-nd |
| 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-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Engineering Science and Technology, an International Journal |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.jestch.2024.101636 |
| primary_location.id | doi:10.1016/j.jestch.2024.101636 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2764510487 |
| primary_location.source.issn | 2215-0986 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2215-0986 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Engineering Science and Technology an International Journal |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Engineering Science and Technology, an International Journal |
| primary_location.landing_page_url | https://doi.org/10.1016/j.jestch.2024.101636 |
| publication_date | 2024-02-09 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2060427373, https://openalex.org/W3006954975, https://openalex.org/W2148309496, https://openalex.org/W2344480160, https://openalex.org/W2923812673, https://openalex.org/W1903029394, https://openalex.org/W6639824700, https://openalex.org/W6753182481, https://openalex.org/W6778979041, https://openalex.org/W4285010746, https://openalex.org/W4287448984, https://openalex.org/W6793010855, https://openalex.org/W6795435739, https://openalex.org/W4200015473, https://openalex.org/W2512138407, https://openalex.org/W6758151433, https://openalex.org/W2143251344, https://openalex.org/W2979688198, https://openalex.org/W6804371142, https://openalex.org/W6749766013, https://openalex.org/W2550409828, https://openalex.org/W2288892845, https://openalex.org/W2344654247, https://openalex.org/W6761049783, https://openalex.org/W2963803174, https://openalex.org/W6783852691, https://openalex.org/W4226057713, https://openalex.org/W6780162839, https://openalex.org/W2955058313, https://openalex.org/W4214666412, https://openalex.org/W3216717225, https://openalex.org/W2194775991, https://openalex.org/W2963855133, https://openalex.org/W2884585870, https://openalex.org/W2752782242, https://openalex.org/W4322747009, https://openalex.org/W3016719260, https://openalex.org/W6740984670, https://openalex.org/W6751420435, https://openalex.org/W2885343725, https://openalex.org/W2592905743, https://openalex.org/W1745334888, https://openalex.org/W3094071141, https://openalex.org/W4283014995, https://openalex.org/W3014974815, https://openalex.org/W6791241316, https://openalex.org/W6802101518, https://openalex.org/W3197957534, https://openalex.org/W6839720089, https://openalex.org/W4382998948, https://openalex.org/W4244955044, https://openalex.org/W3112701542, https://openalex.org/W3193873731, https://openalex.org/W2884436604, https://openalex.org/W4294226146, https://openalex.org/W3036686253, https://openalex.org/W2803187616, https://openalex.org/W2734349601, https://openalex.org/W2605311049, https://openalex.org/W3127751679, https://openalex.org/W3094502228, https://openalex.org/W3014675721, https://openalex.org/W2798122215 |
| referenced_works_count | 63 |
| abstract_inverted_index.a | 158, 203 |
| abstract_inverted_index.In | 0, 51, 238 |
| abstract_inverted_index.We | 155 |
| abstract_inverted_index.an | 56, 163 |
| abstract_inverted_index.as | 130 |
| abstract_inverted_index.be | 112 |
| abstract_inverted_index.by | 161 |
| abstract_inverted_index.in | 6, 90, 101 |
| abstract_inverted_index.is | 44, 243 |
| abstract_inverted_index.of | 2, 22, 30, 48, 67, 106, 125, 151, 210, 253, 262 |
| abstract_inverted_index.on | 188, 214, 219, 225, 250 |
| abstract_inverted_index.to | 19, 62, 76, 121, 258 |
| abstract_inverted_index.we | 54, 94, 116 |
| abstract_inverted_index.and | 8, 26, 69, 85, 141, 178, 182, 194, 206, 212, 217, 221, 223, 235, 256 |
| abstract_inverted_index.for | 137, 147, 233, 246 |
| abstract_inverted_index.has | 201 |
| abstract_inverted_index.not | 111 |
| abstract_inverted_index.one | 38 |
| abstract_inverted_index.our | 198 |
| abstract_inverted_index.the | 20, 28, 35, 64, 82, 87, 91, 96, 103, 118, 123, 131, 138, 142, 148, 152, 230, 240, 251, 254, 260 |
| abstract_inverted_index.two | 119 |
| abstract_inverted_index.via | 72 |
| abstract_inverted_index.Dice | 204 |
| abstract_inverted_index.SOTA | 231 |
| abstract_inverted_index.also | 45, 156, 244 |
| abstract_inverted_index.cost | 89 |
| abstract_inverted_index.deep | 12 |
| abstract_inverted_index.face | 16 |
| abstract_inverted_index.full | 248 |
| abstract_inverted_index.lack | 47 |
| abstract_inverted_index.loss | 21 |
| abstract_inverted_index.meet | 259 |
| abstract_inverted_index.over | 208 |
| abstract_inverted_index.such | 52, 129 |
| abstract_inverted_index.that | 170, 197 |
| abstract_inverted_index.unit | 169 |
| abstract_inverted_index.when | 227 |
| abstract_inverted_index.will | 110 |
| abstract_inverted_index.with | 41, 229 |
| abstract_inverted_index.(CFM) | 146 |
| abstract_inverted_index.(TRM) | 100 |
| abstract_inverted_index.0.713 | 224 |
| abstract_inverted_index.0.741 | 218 |
| abstract_inverted_index.0.829 | 222 |
| abstract_inverted_index.0.855 | 216 |
| abstract_inverted_index.0.869 | 213 |
| abstract_inverted_index.0.923 | 211 |
| abstract_inverted_index.After | 114 |
| abstract_inverted_index.Glas, | 192, 215 |
| abstract_inverted_index.TNBC, | 195, 220 |
| abstract_inverted_index.Union | 209 |
| abstract_inverted_index.boost | 81 |
| abstract_inverted_index.gland | 7, 68, 234, 255 |
| abstract_inverted_index.issue | 40 |
| abstract_inverted_index.layer | 150 |
| abstract_inverted_index.maps, | 80 |
| abstract_inverted_index.model | 200 |
| abstract_inverted_index.needs | 261 |
| abstract_inverted_index.path, | 93 |
| abstract_inverted_index.spite | 1 |
| abstract_inverted_index.split | 166 |
| abstract_inverted_index.still | 15 |
| abstract_inverted_index.that, | 115 |
| abstract_inverted_index.their | 46 |
| abstract_inverted_index.which | 102 |
| abstract_inverted_index.(CFDM) | 136 |
| abstract_inverted_index.(EPSA) | 168 |
| abstract_inverted_index.better | 78 |
| abstract_inverted_index.block. | 154 |
| abstract_inverted_index.deeper | 149 |
| abstract_inverted_index.design | 117, 157 |
| abstract_inverted_index.during | 34 |
| abstract_inverted_index.images | 108 |
| abstract_inverted_index.layers | 140 |
| abstract_inverted_index.modern | 11 |
| abstract_inverted_index.module | 99, 135, 145 |
| abstract_inverted_index.nuclei | 9, 70, 236, 257 |
| abstract_inverted_index.obtain | 77 |
| abstract_inverted_index.paying | 247 |
| abstract_inverted_index.reduce | 86 |
| abstract_inverted_index.reveal | 196 |
| abstract_inverted_index.robust | 176 |
| abstract_inverted_index.MoNuSeg | 193, 226 |
| abstract_inverted_index.address | 122 |
| abstract_inverted_index.cascade | 143 |
| abstract_inverted_index.casting | 162 |
| abstract_inverted_index.context | 24, 127 |
| abstract_inverted_index.decoder | 160 |
| abstract_inverted_index.encoder | 153 |
| abstract_inverted_index.enhance | 63 |
| abstract_inverted_index.feature | 79, 133, 144 |
| abstract_inverted_index.growth, | 84 |
| abstract_inverted_index.images. | 74 |
| abstract_inverted_index.modules | 120 |
| abstract_inverted_index.network | 60, 83 |
| abstract_inverted_index.propose | 55 |
| abstract_inverted_index.pyramid | 165 |
| abstract_inverted_index.quality | 66 |
| abstract_inverted_index.regions | 252 |
| abstract_inverted_index.related | 18 |
| abstract_inverted_index.spatial | 181 |
| abstract_inverted_index.tweaked | 97 |
| abstract_inverted_index.typical | 39 |
| abstract_inverted_index.EMTL-Net | 242 |
| abstract_inverted_index.achieved | 202 |
| abstract_inverted_index.clinical | 49, 263 |
| abstract_inverted_index.compared | 228 |
| abstract_inverted_index.context, | 53 |
| abstract_inverted_index.efficacy | 5 |
| abstract_inverted_index.encoding | 92 |
| abstract_inverted_index.examined | 245 |
| abstract_inverted_index.findings | 187 |
| abstract_inverted_index.learning | 59 |
| abstract_inverted_index.proposed | 199 |
| abstract_inverted_index.regional | 23, 126 |
| abstract_inverted_index.residual | 98 |
| abstract_inverted_index.semantic | 32, 173 |
| abstract_inverted_index.CNN-based | 42 |
| abstract_inverted_index.Extensive | 185 |
| abstract_inverted_index.achieving | 3 |
| abstract_inverted_index.addition, | 239 |
| abstract_inverted_index.attention | 167, 249 |
| abstract_inverted_index.datasets, | 190 |
| abstract_inverted_index.detection | 134 |
| abstract_inverted_index.diagnosis | 104 |
| abstract_inverted_index.disregard | 27 |
| abstract_inverted_index.efficient | 164 |
| abstract_inverted_index.features, | 177 |
| abstract_inverted_index.features. | 184 |
| abstract_inverted_index.including | 191 |
| abstract_inverted_index.introduce | 95 |
| abstract_inverted_index.multitask | 58 |
| abstract_inverted_index.preserves | 171 |
| abstract_inverted_index.structure | 109 |
| abstract_inverted_index.suggested | 241 |
| abstract_inverted_index.(EMTL-Net) | 61 |
| abstract_inverted_index.Initially, | 75 |
| abstract_inverted_index.approaches | 43, 232 |
| abstract_inverted_index.challenges | 17, 124 |
| abstract_inverted_index.importance | 29 |
| abstract_inverted_index.long-range | 31, 172 |
| abstract_inverted_index.real-world | 189 |
| abstract_inverted_index.scenarios. | 264 |
| abstract_inverted_index.stimulates | 175 |
| abstract_inverted_index.techniques | 14 |
| abstract_inverted_index.Coefficient | 205 |
| abstract_inverted_index.competitive | 159 |
| abstract_inverted_index.dynamically | 179 |
| abstract_inverted_index.explainable | 57 |
| abstract_inverted_index.human-level | 4 |
| abstract_inverted_index.information | 25, 33, 105 |
| abstract_inverted_index.overlooked. | 113 |
| abstract_inverted_index.Furthermore, | 37 |
| abstract_inverted_index.Intersection | 207 |
| abstract_inverted_index.channel-wise | 183 |
| abstract_inverted_index.experimental | 186 |
| abstract_inverted_index.information, | 128, 174 |
| abstract_inverted_index.intermediate | 139 |
| abstract_inverted_index.recalibrates | 180 |
| abstract_inverted_index.segmentation | 65, 71 |
| abstract_inverted_index.complementary | 132 |
| abstract_inverted_index.computational | 88 |
| abstract_inverted_index.optimization. | 36 |
| abstract_inverted_index.segmentation, | 10 |
| abstract_inverted_index.segmentation. | 237 |
| abstract_inverted_index.histopathology | 73, 107 |
| abstract_inverted_index.explainability. | 50 |
| abstract_inverted_index.learning-driven | 13 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.92374937 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |