Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:04:55.390764Z
Paper Citation Record · LEDGER
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images
As of 21 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:2507.11886.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-18T20:22:41.555806Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T20:22:50.710480Z
Reference resolution
25 of 25 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation d7cd12a8-f193-4b6b-aa60-663723b9523e · outbound
This paper cites The global burden of cardiovascular diseases and risk: a compass for future health, 2022.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images The global burden of cardiovascular diseases and risk: a compass for future health, 2022
Reference 1
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 3a070b09-5eca-4f53-9b7b-f3bada3e56b8 · outbound
This paper cites CardioCoT: Hierarchical Reasoning for Multimodal Survival Analysis.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images CardioCoT: Hierarchical Reasoning for Multimodal Survival Analysis
Reference 2
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9237737e-8a29-45bb-bc22-8125b9928830 · outbound
This paper cites Desam: Decoupled segment anything model for generalizable medical image segmentation.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Desam: Decoupled segment anything model for generalizable medical image segmentation
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:04:59.393501Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b11d17d0-d27a-4448-b62b-446f38f1ec64 · outbound
This paper cites Mba-net: Sam-driven bidirec- tional aggregation network for ovarian tumor segmentation.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Mba-net: Sam-driven bidirec- tional aggregation network for ovarian tumor segmentation
Reference 4
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 729438f2-a9b5-48c7-9920-52c4e3d7d24e · outbound
This paper cites Prognostic value of cardiac mri late gadolinium enhancement granularity in participants with ischemic cardiomyopathy.Radiology, 314(1):e240806, 2025.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Prognostic value of cardiac mri late gadolinium enhancement granularity in participants with ischemic cardiomyopathy.Radiology, 314(1):e240806, 2025
Reference 5
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0d1ac856-5346-4157-b1d9-9039731f55e7 · outbound
This paper cites an unresolved cited work.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Unresolved cited work
Reference 6
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 752e865e-2302-41e4-b4ed-81a38fe0e043 · outbound
This paper cites Clinical impact of cardiac mri t1 and t2 parametric mapping in patients with suspected cardiomyopathy.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Clinical impact of cardiac mri t1 and t2 parametric mapping in patients with suspected cardiomyopathy
Reference 7
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d78c3af0-5293-4298-b296-f37861418d67 · outbound
This paper cites An anatomy-aware framework for automatic segmentation of parotid tumor from multimodal mri.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images An anatomy-aware framework for automatic segmentation of parotid tumor from multimodal mri
Reference 8
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 74241744-54c8-4680-9cf9-ba90c0fec5b1 · outbound
This paper cites Transmed: Transformers advance multi-modal medical image classification.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Transmed: Transformers advance multi-modal medical image classification
Reference 9
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6cf51282-b2a9-419e-90f3-56e605292ad5 · outbound
This paper cites Cardiac seg- mentation from lge mri using deep neural network incorporating shape and spatial priors.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Cardiac seg- mentation from lge mri using deep neural network incorporating shape and spatial priors
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:04:57.851306Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 15c97de6-4a92-45d1-b319-41a9c6ff68f3 · outbound
This paper cites Car- diac lge mri segmentation with cross-modality image augmentation and improved u-net.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Car- diac lge mri segmentation with cross-modality image augmentation and improved u-net
Reference 11
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 02e425f1-9928-4089-ab54-c28bb2cc5628 · outbound
This paper cites Medical image analysis on left atrial lge mri for atrial fibrillation studies: A review.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Medical image analysis on left atrial lge mri for atrial fibrillation studies: A review
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:04:57.408868Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 931b02f7-b8ed-4723-aa2b-ce4e6fdea779 · outbound
This paper cites Max-fusion u-net for multi-modal pathology segmentation with attention and dynamic resampling.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Max-fusion u-net for multi-modal pathology segmentation with attention and dynamic resampling
Reference 13
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 95096c75-56e5-426b-b704-5e0f49135e40 · outbound
This paper cites Awsnet: An auto-weighted su- pervision attention network for myocardial scar and edema segmentation in multi- sequence cardiac magnetic resonance images.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Awsnet: An auto-weighted su- pervision attention network for myocardial scar and edema segmentation in multi- sequence cardiac magnetic resonance images
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:04:57.061766Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2d1de477-c7f1-4d70-8242-59b94369d630 · outbound
This paper cites Myops-net: Myocardial pathology segmentation with flexible combination of multi-sequence cmr images.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Myops-net: Myocardial pathology segmentation with flexible combination of multi-sequence cmr images
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:04:56.857446Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation b0c20d06-20b8-4ad7-985a-72412e6a3fda · outbound
This paper cites Multi-modal disease segmentation with continual learning and adaptive decision fusion.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Multi-modal disease segmentation with continual learning and adaptive decision fusion
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:04:56.746493Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2d165835-3d40-4756-8a85-6e7a24a968b8 · outbound
This paper cites Myops: A benchmark of myocardial pathology segmentation combining three-sequence car- diac magnetic resonance images.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Myops: A benchmark of myocardial pathology segmentation combining three-sequence car- diac magnetic resonance images
Reference 17
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a858f337-994f-45ec-b291-1bbbd2f04578 · outbound
This paper cites Uniseg: A prompt-driven universal segmentation model as well as a strong representation learner.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Uniseg: A prompt-driven universal segmentation model as well as a strong representation learner
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:04:56.507714Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation bf0da884-e585-4f80-9a37-5ca13ac6d54f · outbound
This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Reference 19
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 9f099eef-adc5-482d-9431-6f61bb9e2fd9 · outbound
This paper cites Unet++: A nested u-net architecture for medical image segmentation.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Unet++: A nested u-net architecture for medical image segmentation
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:04:56.168435Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 216eb646-993b-4c05-a577-e2fb52e35a23 · outbound
This paper cites Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Transunet: Rethinking the u-net architecture design for medical image segmentation through the lens of transformers
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:04:56.030878Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 42eddad4-c4f2-4309-aac4-496538fc9f98 · outbound
This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:04:55.853091Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d82c0819-6205-41cd-a7b6-b7de5f864210 · outbound
This paper cites Utnet: a hybrid transformer ar- chitecture for medical image segmentation.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Utnet: a hybrid transformer ar- chitecture for medical image segmentation
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:04:55.693568Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2fd72cfc-ef51-4ab7-8ed9-798d1e065df3 · outbound
This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation
Reference 24
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2d025fc3-2333-4cad-a090-71f6b3eed9c9 · outbound
This paper cites Multivariate mixture model for myocardial segmentation com- bining multi-source images.
A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images Multivariate mixture model for myocardial segmentation com- bining multi-source images
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:04:55.546557Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Pith citing papers
Observation 124bdc7c-71c1-4d01-a3a9-e0b2a1483bde · inbound
Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation cites this paper.
Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T20:22:50.714116Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.