Paper Citation Record · LEDGER

A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images

As of 8 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.

pith.paper-citation-record.v1
2507.11886 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:04:55.390764Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-18T20:22:41.555806Z

measured 0 of 1 external citation measurements

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; raw_fallback, observed 2026-08-06T17:04:59.643392Z

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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

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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

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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; raw_fallback, observed 2026-08-06T17:04:59.143031Z

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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; raw_fallback, observed 2026-08-06T17:04:59.008201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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; raw_fallback, observed 2026-08-06T17:04:58.797622Z

Source-reported events for the cited work

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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; raw_fallback, observed 2026-08-06T17:04:58.548458Z

Source-reported events for the cited work

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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; raw_fallback, observed 2026-08-06T17:04:58.232603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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; raw_fallback, observed 2026-08-06T17:04:58.043567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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; raw_fallback, observed 2026-08-06T17:04:57.618059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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

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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; raw_fallback, observed 2026-08-06T17:04:57.215686Z

Source-reported events for the cited work

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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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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

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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; raw_fallback, observed 2026-08-06T17:04:56.634154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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; raw_fallback, observed 2026-08-06T17:04:56.369838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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; no resolver link, observed 2026-08-06T17:04:55.289253Z

Source-reported events for the cited work

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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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+00:00.