Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T13:58:53.834808Z
Paper Citation Record · LEDGER
As of 13 August 2026, this Paper Citation Record lists 89 of 89 outbound references and 0 inbound Pith citation observations for arXiv:2411.15763.
A citation records a reference. It does not transfer a finding from one paper to another.
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Source: paper_references, paper_reference_links, observed 2026-08-12T13:58:53.834808Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
Source: cited_works
89 of 89 outbound references displayed
External citation measurements
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Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation Annotation-efficient deep learning for automatic medical image segmentation
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Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation Scribble2d5: Weakly-supervised volumetric image segmentation via scribble annotations
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Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing
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Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation Scribble-supervised medical image segmentation via dual-branch network and dynamically mixed pseudo labels supervision
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Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
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Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation Lvm-med: Learning large-scale self-supervised vision models for medical imaging via second-order graph matching
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Observation 79f93cbc-5a6f-41d4-894e-36ca918fb5ab · outbound
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging, 37(11):2514–2525, 2018
Reference 78
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Observation 08d74b8c-37e4-4e6e-ab02-9f070c411e33 · outbound
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation Alper Selver, O˘guz Dicle, Mustafa Barı¸ s, and N
Reference 79
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Observation ccf38707-e61a-4f46-872c-b75aa75eb9a8 · outbound
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation Bayeseg: Bayesian modeling for medical image segmentation with interpretable generalizability, 2023
Reference 80
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Observation 0598ff89-c2d5-4c86-8551-3d824342a263 · outbound
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation Multivariate mixture model for myocardial segmentation combining multi- source images
Reference 81
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Observation bd3c5c17-cc4d-4c8d-a795-51bcebfb3fc2 · outbound
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation Minimizing estimated risks on unlabeled data: A new formula- tion for semi-supervised medical image segmentation
Reference 82
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Observation 41e3df8b-a875-4b64-b331-7a2aadd85d2e · outbound
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision
Reference 83
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Observation 1415cff2-7bd8-47dd-b563-0246dc6bbf81 · outbound
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation A benchmark dataset and evaluation methodology for video object segmentation
Reference 84
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Observation 64f37caf-1be4-4cb2-8d25-2c343a89fb7c · outbound
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation The 2017 DAVIS Challenge on Video Object Segmentation
Reference 85
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Observation 35774759-8cf3-49f3-bb60-863d924dc325 · outbound
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation Reliable delineation of clinical target volumes for cervical cancer radiotherapy on ct/mr dual-modality images
Reference 86
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Observation 5e969f19-61fe-419b-b415-bb5033861aad · outbound
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation Domain and User-Centered Machine Learning for Medical Image Analysis
Reference 87
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Observation deb35c5f-e1a1-49d6-b579-dca9b55aedfb · outbound
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation Deep learning algorithm for auto-delineation of high-risk oropharyngeal clinical target volumes with built-in dice similarity coefficient parameter optimization function
Reference 88
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Observation e4691c2a-c8bc-4328-8028-35763be2c596 · outbound
Integrating Deep Metric Learning with Coreset for Active Learning in 3D Segmentation Automatic detection of contouring errors using convolutional neural networks
Reference 89
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No inbound Pith citation observations are available.