Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-03T15:57:58.162934Z
Paper Citation Record · LEDGER
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision
As of 22 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2607.02051.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
42 of 42 outbound references displayed
- verified exact1
- verified fuzzy39
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 6b5cbcfc-274a-48cb-9f48-f863d8ea6867 · outbound
This paper cites Semi- supervised information fusion for medical image analysis: Recent progress and future perspectives.Information Fusion, 106:102263, 2024.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Semi- supervised information fusion for medical image analysis: Recent progress and future perspectives.Information Fusion, 106:102263, 2024
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.748778Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation af5adb24-bf2a-4eeb-86f2-39f1067470db · outbound
This paper cites Deep semi-supervised learning for medical image segmentation: A review.Expert Systems with Applications, page 123052, 2024.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Deep semi-supervised learning for medical image segmentation: A review.Expert Systems with Applications, page 123052, 2024
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.750848Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation b15443c0-aa20-49df-b712-2b3a5ec359fb · outbound
This paper cites Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.728884Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 6847dde3-7a38-455f-93be-71193d04e957 · outbound
This paper cites Uncertainty-aware self-ensembling model for semi- supervised3dleftatriumsegmentation.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Uncertainty-aware self-ensembling model for semi- supervised3dleftatriumsegmentation
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.736665Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8cc36663-9047-4812-9b00-06eb97257ba8 · outbound
This paper cites Semi- supervised medical image segmentation through dual-task consis- tency.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Semi- supervised medical image segmentation through dual-task consis- tency
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.746808Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation a2c8220f-33d0-4156-b1d0-bb29eceb94b9 · outbound
This paper cites Triple-task mutual consistency for semi-supervised 3d medical image segmentation.Computers in Biology and Medicine, 175:108506, 2024.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Triple-task mutual consistency for semi-supervised 3d medical image segmentation.Computers in Biology and Medicine, 175:108506, 2024
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.772937Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 21cb2e67-21bd-4eaa-b558-2f673fe1f8fc · outbound
This paper cites Upcol: uncertainty-informed prototype con- sistency learning for semi-supervised medical image segmentation.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Upcol: uncertainty-informed prototype con- sistency learning for semi-supervised medical image segmentation
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.704453Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2d566858-2e39-4f34-a9fb-609593e0883b · outbound
This paper cites All-around reallabelsupervision:Cyclicprototypeconsistencylearningforsemi- supervisedmedicalimagesegmentation.IEEEJournalofBiomedical and Health Informatics, 26(7):3174–3184, 2022.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision All-around reallabelsupervision:Cyclicprototypeconsistencylearningforsemi- supervisedmedicalimagesegmentation.IEEEJournalofBiomedical and Health Informatics, 26(7):3174–3184, 2022
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.711246Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 4ada52b0-6336-4952-84d1-ba4e050089a6 · outbound
This paper cites Boundary-aware prototype in semi-supervised medical image seg- mentation.IEEE Transactions on Image Processing, 2024.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Boundary-aware prototype in semi-supervised medical image seg- mentation.IEEE Transactions on Image Processing, 2024
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.722739Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 415596bf-b466-4ba0-9142-c1a7de5614ce · outbound
This paper cites InICASSP 2025 - 2025 IEEE International Conference on Acoustics,SpeechandSignalProcessing(ICASSP),pages1–5,2025.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision InICASSP 2025 - 2025 IEEE International Conference on Acoustics,SpeechandSignalProcessing(ICASSP),pages1–5,2025
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.681412Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 1ac520e3-a63f-4016-b65b-d5f3825b8edf · outbound
This paper cites Im- proving segmentation and detection of lesions in ct scans using intensity distribution supervision.Computerized Medical Imaging and Graphics, 108:102259, 2023.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Im- proving segmentation and detection of lesions in ct scans using intensity distribution supervision.Computerized Medical Imaging and Graphics, 108:102259, 2023
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.683614Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5d74f1bb-d06f-4f3b-b5ab-dc153b145b8f · outbound
This paper cites Comprehensive evaluation of op- timization algorithms for medical image segmentation.Scientific Reports, 15(1):37190, 2025.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Comprehensive evaluation of op- timization algorithms for medical image segmentation.Scientific Reports, 15(1):37190, 2025
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.685796Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 6c3935d1-35ef-4dd5-a7a4-2cf4d4ed4486 · outbound
This paper cites Gaussian mixture models.Encyclopedia of biometrics, 741(659-663):3, 2009.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Gaussian mixture models.Encyclopedia of biometrics, 741(659-663):3, 2009
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.720968Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 80af79a7-75cd-4192-bf3b-a7c1ed141e3b · outbound
This paper cites Constrained gaussianmixturemodelframeworkforautomaticsegmentationofmr brain images.IEEE transactions on medical imaging, 25(9):1233– 1245, 2006.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Constrained gaussianmixturemodelframeworkforautomaticsegmentationofmr brain images.IEEE transactions on medical imaging, 25(9):1233– 1245, 2006
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.677277Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 65b6d4d2-725c-4a00-bec6-efcce3ac7437 · outbound
This paper cites Cross-domain medicalimagetranslationbysharedlatentgaussianmixturemodel.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Cross-domain medicalimagetranslationbysharedlatentgaussianmixturemodel
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.679308Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation bbafe142-0945-492b-8076-83669cac0e0d · outbound
This paper cites Rednet: Reliable evidential discounting network for multi- modality medical image segmentation.IEEE Transactions on Medi- cal Imaging, 2025.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Rednet: Reliable evidential discounting network for multi- modality medical image segmentation.IEEE Transactions on Medi- cal Imaging, 2025
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.713101Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation df2229c7-1320-4037-931c-33bf2684d716 · outbound
This paper cites Target-aware u-net with fuzzy skip connec- tions for refined pancreas segmentation.Applied Soft Computing, 131:109818, 2022.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Target-aware u-net with fuzzy skip connec- tions for refined pancreas segmentation.Applied Soft Computing, 131:109818, 2022
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.774858Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0d94def5-89ba-4873-8909-db52c9b0a93f · outbound
This paper cites Medical image segmentation review: The success of u-net.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Medical image segmentation review: The success of u-net.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.770684Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 3332d2fa-fde8-4938-8aab-aefd4ada8614 · outbound
This paper cites D-edl: Differ- ential evidential deep learning for robust medical out-of-distribution detection.Medical Image Analysis, page 103888, 2025.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision D-edl: Differ- ential evidential deep learning for robust medical out-of-distribution detection.Medical Image Analysis, page 103888, 2025
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.744977Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 9620a17e-5474-44c9-8a44-6f99831461f7 · outbound
This paper cites Category-specific unlabeled data risk minimization for ultrasound semi-supervised segmentation.Medical Image Analysis, page 103773, 2025.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Category-specific unlabeled data risk minimization for ultrasound semi-supervised segmentation.Medical Image Analysis, page 103773, 2025
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.740669Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation ed5cc048-eeea-4eb5-a49f-76ad5d534c90 · outbound
This paper cites SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation Models.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision SemiSAM+: Rethinking Semi-Supervised Medical Image Segmentation in the Era of Foundation Models
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T15:58:37.244878Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0337a305-679e-4165-9bbe-827bc3d9baed · outbound
This paper cites Semi-supervisedmedicalimagesegmentationviauncertainty rectified pyramid consistency.Medical Image Analysis, 80:102517, 2022.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Semi-supervisedmedicalimagesegmentationviauncertainty rectified pyramid consistency.Medical Image Analysis, 80:102517, 2022
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.738660Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation b764178c-80d7-4c7a-9297-5d2e3ba1a43f · outbound
This paper cites InIn- ternationalConferenceonMedicalImageComputingandComputer- Assisted Intervention, pages 481–491.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision InIn- ternationalConferenceonMedicalImageComputingandComputer- Assisted Intervention, pages 481–491
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.744771Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 6faa5bdc-5719-48ef-8361-75137300f093 · outbound
This paper cites Temporal ensembling for semi- supervisedlearning.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Temporal ensembling for semi- supervisedlearning
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.732487Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation c42d9810-c29a-427d-8c43-8458f42286a7 · outbound
This paper cites an unresolved cited work.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Unresolved cited work
Reference 25
- Resolution
- unresolved
- raw_fallback, observed 2026-07-05T05:50:44.730736Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0167b22c-a357-4eba-8b8d-bb8cb0f9318c · outbound
This paper cites Semi-supervised 3d medical image segmentation based on dual-task consistent joint learning and task-level regularization.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Semi-supervised 3d medical image segmentation based on dual-task consistent joint learning and task-level regularization
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.724735Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation dfa7c62e-06fd-4e2d-aba7-78e8aadc26da · outbound
This paper cites Adaptive feature aggregation based multi-task learning for uncertainty-guided semi-supervised medical image segmentation.Expert Systems with Applications, 232:120836, 2023.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Adaptive feature aggregation based multi-task learning for uncertainty-guided semi-supervised medical image segmentation.Expert Systems with Applications, 232:120836, 2023
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.732708Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation efee3a4f-294a-41a1-bf92-ec33daf91b37 · outbound
This paper cites Self- supervisedcorrectionlearningforsemi-supervisedbiomedicalimage segmentation.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Self- supervisedcorrectionlearningforsemi-supervisedbiomedicalimage segmentation
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.734574Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d517a157-141e-4d1f-b4f8-1ab660fe6408 · outbound
This paper cites Semi-supervised left atrium segmentation with mutual consistency training.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Semi-supervised left atrium segmentation with mutual consistency training
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.752760Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 58e9c052-a46d-4c42-82b9-42628c20b549 · outbound
This paper cites Few-shot semantic segmentation with prototype learning.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Few-shot semantic segmentation with prototype learning
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.716762Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation f202ac9d-2994-4ea2-9a5a-9686978dcfa1 · outbound
This paper cites Psanet: prototype-guided salient attention for few-shot segmentation.The Visual Computer, pages 1–15, 2024.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Psanet: prototype-guided salient attention for few-shot segmentation.The Visual Computer, pages 1–15, 2024
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.714940Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation db810a9c-2f68-4f56-be09-91c575983a6e · outbound
This paper cites Kp2l: Knowledge-driven pyramid prototype learning for semi- supervised medical image segmentation.Knowledge-Based Systems, 340:115662, 2026.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Kp2l: Knowledge-driven pyramid prototype learning for semi- supervised medical image segmentation.Knowledge-Based Systems, 340:115662, 2026
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.719056Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 184ce2fd-8793-4216-b9cf-0028aa96f447 · outbound
This paper cites Semi- supervised semantic segmentation with prototype-based consistency regularization.Advances in neural information processing systems, 35:26007–26020, 2022.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Semi- supervised semantic segmentation with prototype-based consistency regularization.Advances in neural information processing systems, 35:26007–26020, 2022
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.720283Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 19b10085-b4df-44f2-b4ec-aa5411ec6fcd · outbound
This paper cites Adaptiveprototype learningand allocation for few-shot segmentation.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Adaptiveprototype learningand allocation for few-shot segmentation
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.722244Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation ff2d219c-30c7-4bfa-88f7-21e31f9fb308 · outbound
This paper cites an unresolved cited work.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Unresolved cited work
Reference 35
- Resolution
- unresolved
- raw_fallback, observed 2026-07-05T05:50:44.730322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7ab113d9-5d7c-4aeb-8cb9-65d32b884e40 · outbound
This paper cites Mvpcl: multi-view prototype consis- tencylearningforsemi-supervisedmedicalimagesegmentation.The Visual Computer, pages 1–14, 2024.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Mvpcl: multi-view prototype consis- tencylearningforsemi-supervisedmedicalimagesegmentation.The Visual Computer, pages 1–14, 2024
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.700608Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 32573bbf-929e-4eae-8827-54db39d48123 · outbound
This paper cites Focal loss for dense object detec- tion.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Focal loss for dense object detec- tion
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.734586Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0ec349fe-2e4a-4cd2-82ce-00e14e7ddba3 · outbound
This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision V-net: Fully convolutional neural networks for volumetric medical image segmentation
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.742996Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation bb273ca3-8592-4aa6-bd50-6926555b9406 · outbound
This paper cites Unitbox:Anadvancedobjectdetectionnetwork.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Unitbox:Anadvancedobjectdetectionnetwork
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.691885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2d5525d1-eb60-4180-b788-8aefbc1cf9ff · outbound
This paper cites A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic res- onance imaging.Medical image analysis, 67:101832, 2021.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic res- onance imaging.Medical image analysis, 67:101832, 2021
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.696713Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 97529ad3-d764-4231-8a76-0374c308b18d · outbound
This paper cites Data from pancreas-ct.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision Data from pancreas-ct
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.690065Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 1fe46ce8-783b-48a8-a195-3eb66cfc36c4 · outbound
This paper cites The multimodal brain tumor image segmentation benchmark (brats).IEEE transactions on medical imaging, 34(10):1993–2024, 2014.
Embracing Intra-Class Heterogeneity for Semi-Supervised Medical Image Segmentation: From Diversity to Precision The multimodal brain tumor image segmentation benchmark (brats).IEEE transactions on medical imaging, 34(10):1993–2024, 2014
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-05T05:50:44.688257Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Pith citing papers
No inbound Pith citation observations are available.