Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T20:37:45.912152Z
Paper Citation Record · LEDGER
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation
As of 21 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2507.02399.
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 2026-08-21T06:32:19.484+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
43 of 43 outbound references displayed
- verified exact0
- verified fuzzy28
- unresolved15
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 66776800-a632-4f04-a7ce-84a3c3da24e4 · outbound
This paper cites an unresolved cited work.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Unresolved cited work
Reference 1
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:37:52.757408Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation bd83c80a-638e-433b-af94-6ede5d18c1fb · outbound
This paper cites Semi-supervisedmedicalimagesegmentationvialearning consistencyundertransformations,in:MedicalImageComputingand Computer Assisted Intervention, Springer.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Semi-supervisedmedicalimagesegmentationvialearning consistencyundertransformations,in:MedicalImageComputingand Computer Assisted Intervention, Springer
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:52.421201Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation fa845389-0d0b-4509-b184-cb4dff068239 · outbound
This paper cites an unresolved cited work.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Unresolved cited work
Reference 3
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:37:52.296966Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c8ea2274-473d-4d36-8bd4-f07115a71720 · outbound
This paper cites Ad- dressinginconsistentlabelingwithcrossimagematchingforscribble- based medical image segmentation.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Ad- dressinginconsistentlabelingwithcrossimagematchingforscribble- based medical image segmentation
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:51.990484Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 91325d73-ead5-4d09-b737-28c2e3e56df3 · outbound
This paper cites A survey on deep learning in medical imageregistration:Newtechnologies,uncertainty,evaluationmetrics, and beyond.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation A survey on deep learning in medical imageregistration:Newtechnologies,uncertainty,evaluationmetrics, and beyond
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:51.803041Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 7575e2bb-846b-4e18-bf09-e9aa329d3611 · outbound
This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Improved Regularization of Convolutional Neural Networks with Cutout
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:37:42.947235Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e734e220-13c2-4ddf-b495-7f389bfefdb0 · outbound
This paper cites an unresolved cited work.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Unresolved cited work
Reference 7
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:37:51.535226Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 7239ad83-d639-462d-a162-1e6825452ed0 · outbound
This paper cites DMSPS: Dynamically mixed soft pseudo- label supervision for scribble-supervised medical image segmenta- tion.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation DMSPS: Dynamically mixed soft pseudo- label supervision for scribble-supervised medical image segmenta- tion
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:51.401718Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a15690d0-4b77-4c80-95ed-ee97aeea726a · outbound
This paper cites Medical Image Analysis 91, 102984.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Medical Image Analysis 91, 102984
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:51.295416Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 45f43bbf-6ea5-42c2-afe5-447e2c4b8a10 · outbound
This paper cites Generative feature style aug- mentation for domain generalization in medical image segmentation.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Generative feature style aug- mentation for domain generalization in medical image segmentation
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:51.141092Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 078e737c-d05c-4750-bae5-ee9cb7f7e882 · outbound
This paper cites an unresolved cited work.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Unresolved cited work
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:37:43.210791Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ffa98104-ee76-41f3-bb05-5fe8184de73f · outbound
This paper cites Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:37:43.334821Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a5746d35-362c-49e2-884a-e3fe8edfeca2 · outbound
This paper cites Puzzle mix: Exploiting saliency and local statistics for optimal mixup, in: International Con- ference on Machine Learning, PMLR.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Puzzle mix: Exploiting saliency and local statistics for optimal mixup, in: International Con- ference on Machine Learning, PMLR
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:50.984213Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation b290e580-f9d1-435b-8e7b-9a1f35ac09c4 · outbound
This paper cites Data augmentationtechniquesformedicalimagesegmentation–areview, in:2024InternationalConferenceonComputerandApplications,pp.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Data augmentationtechniquesformedicalimagesegmentation–areview, in:2024InternationalConferenceonComputerandApplications,pp
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:50.809664Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation fab5c737-1f88-47d0-b80a-bd447cd07ff7 · outbound
This paper cites an unresolved cited work.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Unresolved cited work
Reference 15
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:37:50.662710Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 18191d16-5c32-4608-bdf6-0fc3f8448345 · outbound
This paper cites Transformation-consistentself-ensemblingmodelforsemisupervised medicalimagesegmentation.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Transformation-consistentself-ensemblingmodelforsemisupervised medicalimagesegmentation
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:50.438494Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation d21fb5a7-cbe0-4540-a572-f299b2d0597b · outbound
This paper cites ScribbleVC: Scribble-supervised medical image segmentation with vision-class embedding, in: Proceedings of the 31st ACM International Confer- ence on Multimedia, p.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation ScribbleVC: Scribble-supervised medical image segmentation with vision-class embedding, in: Proceedings of the 31st ACM International Confer- ence on Multimedia, p
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:50.238462Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 2f4a82ec-b0c0-4931-b661-814826563b6e · outbound
This paper cites Scribformer: Transformer makes CNN work better for scribble-based medical image segmentation.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Scribformer: Transformer makes CNN work better for scribble-based medical image segmentation
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:50.091237Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation b8956853-e064-4b77-9aaf-2dd13b1e0221 · outbound
This paper cites SSFam: Scribble supervised salient object detection family.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation SSFam: Scribble supervised salient object detection family
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:49.902649Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation df6f34f3-4881-486d-918a-6a607f617a27 · outbound
This paper cites an unresolved cited work.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Unresolved cited work
Reference 20
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:37:49.652791Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 90a44a95-a61e-4661-b149-9da9bf88d387 · outbound
This paper cites QMaxViT-Unet+: A query-based MaxViT-Unet with edge enhancement for scribble- supervised segmentation of medical images.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation QMaxViT-Unet+: A query-based MaxViT-Unet with edge enhancement for scribble- supervised segmentation of medical images
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:49.448248Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a37cf154-3c51-4809-abf0-e80bb30c96ae · outbound
This paper cites U-Net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention, Springer.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation U-Net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention, Springer
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:37:44.059443Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b220bfb7-c074-43da-8010-13bf3abe6669 · outbound
This paper cites Mutuallearningwithre- liablepseudolabelforsemi-supervisedmedicalimagesegmentation.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Mutuallearningwithre- liablepseudolabelforsemi-supervisedmedicalimagesegmentation
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:49.287483Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c546aabd-d16a-457c-81d5-3b3d0ff90698 · outbound
This paper cites Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:49.059387Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 93f2df24-337a-4fb7-82d6-6571e82dce5a · outbound
This paper cites Deeplearningonmedicalimage analysis.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Deeplearningonmedicalimage analysis
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:48.794475Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 56a63728-af86-4a8b-8d36-ad6913569c53 · outbound
This paper cites Competetowin:Enhanc- ingpseudolabelsforbarely-supervisedmedicalimagesegmentation.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Competetowin:Enhanc- ingpseudolabelsforbarely-supervisedmedicalimagesegmentation
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:48.585080Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8f0d0936-ada6-4ddc-b85c-c9efa4e611f3 · outbound
This paper cites Gaze- directed vision GNN for mitigating shortcut learning in medical image,in:InternationalConferenceonMedicalImageComputingand Computer-Assisted Intervention, Springer.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Gaze- directed vision GNN for mitigating shortcut learning in medical image,in:InternationalConferenceonMedicalImageComputingand Computer-Assisted Intervention, Springer
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:48.412446Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation bda220a9-b94d-46c5-a3a3-09e21f95a24a · outbound
This paper cites an unresolved cited work.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Unresolved cited work
Reference 28
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:37:48.244098Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8d623103-c15d-42a0-9cb4-2ad10976179c · outbound
This paper cites Non-iterative scribble- supervised learning with pacing pseudo-masks for medical image segmentation.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Non-iterative scribble- supervised learning with pacing pseudo-masks for medical image segmentation
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:47.800224Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 71ae6f58-801d-40af-8213-8bd107e437ed · outbound
This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Cutmix: Regularization strategy to train strong classifiers with localizable features, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:47.654777Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 28943385-bb92-4608-ab4a-2d5302f5a60b · outbound
This paper cites mixup: Beyond Empirical Risk Minimization.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation mixup: Beyond Empirical Risk Minimization
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:37:45.068244Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a5e75033-49d3-4b4d-b7c5-0a76424058fc · outbound
This paper cites Computers in Biology and Medicine 168, 107744.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Computers in Biology and Medicine 168, 107744
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:47.997129Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8cc2eb22-4664-4678-88fa-70fe4ba337f4 · outbound
This paper cites an unresolved cited work.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Unresolved cited work
Reference 33
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:37:47.287753Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 37a51cda-b75f-41bd-98a2-2f19323e6829 · outbound
This paper cites An anatomy-and topology-preserving framework for coronary artery segmentation.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation An anatomy-and topology-preserving framework for coronary artery segmentation
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:47.169346Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation b4bb8968-f48e-478b-acc6-eba7cc3352dc · outbound
This paper cites Scribblehidesclass: Promoting scribble-based weakly-supervised semantic segmentation with its class label, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Scribblehidesclass: Promoting scribble-based weakly-supervised semantic segmentation with its class label, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:47.011617Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 02480239-37be-42a4-b6ac-d838af618c01 · outbound
This paper cites CycleMix: A holistic strategy for medical image segmentation from scribble supervision, in: Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation CycleMix: A holistic strategy for medical image segmentation from scribble supervision, in: Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:47.419863Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation dd06f8a6-35fb-49ee-a025-c033a3c9ef70 · outbound
This paper cites UNet++: A nested U-Net architecture for medical image segmenta- tion, in: Deep learning in Medical Image Analysis and Multimodal Learning For Clinical Decision Support, Springer.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation UNet++: A nested U-Net architecture for medical image segmenta- tion, in: Deep learning in Medical Image Analysis and Multimodal Learning For Clinical Decision Support, Springer
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:46.292930Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 45b19f63-f77f-470f-9427-7bf9578e3beb · outbound
This paper cites Multivariate mixture model for myocardial seg- mentation combining multi-source images.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Multivariate mixture model for myocardial seg- mentation combining multi-source images
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:46.146162Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 964af938-efae-474c-9e7b-0feb920ce961 · outbound
This paper cites an unresolved cited work.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Unresolved cited work
Reference 40
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:37:46.821183Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 298d3fd5-7a54-4908-b967-3f40a1f2ff53 · outbound
This paper cites EngineeringAppli- cations of Artificial Intelligence 130, 107777.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation EngineeringAppli- cations of Artificial Intelligence 130, 107777
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:46.607655Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation ece0c349-b281-4afc-aa10-b7ba6a5ec358 · outbound
This paper cites an unresolved cited work.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Unresolved cited work
Reference 2018
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:37:52.579125Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 90adb4b0-2577-4b49-b08b-da2da57b028f · outbound
This paper cites Nature Methods 18, 203–211.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Nature Methods 18, 203–211
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:37:43.288437Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5649caf8-0fad-40b1-9b84-43391c642aba · outbound
This paper cites Pattern Recognition 145, 109881.
TABNet: A Triplet Augmentation Self-Recovery Framework with Boundary-Aware Pseudo-Labels for Medical Image Segmentation Pattern Recognition 145, 109881
Reference 2024
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:37:52.144239Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Pith citing papers
No inbound Pith citation observations are available.