Paper Citation Record · LEDGER

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation

As of 9 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2507.23326.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.23326 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:56:51.519688Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e5521e40-5465-444a-a824-252f9f3840ac · outbound

This paper cites A survey on deep learning in medical image analysis,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation A survey on deep learning in medical image analysis,

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f9bf5b0-f934-4d22-a909-2b24e7738b22 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T10:56:51.412777Z

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Observation 29a6c63d-2205-4ee8-8995-0f3da84bccb1 · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,

Reference 3

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Source-reported events for the cited work

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Observation 045cf4dd-bd33-4964-8436-723ca4ce64fb · outbound

This paper cites Unetr: Transformers for 3d medical image segmentation,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Unetr: Transformers for 3d medical image segmentation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.841585Z

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Observation c4648584-3943-4ad4-80db-728101567e1c · outbound

This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,

Reference 5

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Observation 3718925c-13f1-4be8-b43a-4d6e7711b943 · outbound

This paper cites Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects

Reference 6

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Source-reported events for the cited work

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Observation bd4d9c92-fc07-479c-9731-90a54850ce88 · outbound

This paper cites Domain generalization for medical image analysis: A review,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Domain generalization for medical image analysis: A review,

Reference 7

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Observation 7412c44b-d1f1-4253-ae37-c3765bf5956b · outbound

This paper cites Proto- typical pseudo label denoising and target structure learning for domain adaptive semantic segmentation,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Proto- typical pseudo label denoising and target structure learning for domain adaptive semantic segmentation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.818301Z

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Observation 2f19faee-0373-48a4-9552-f62840f99b7e · outbound

This paper cites Self-attentive spatial adaptive normalization for cross-modality domain adaptation,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Self-attentive spatial adaptive normalization for cross-modality domain adaptation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.808088Z

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Observation b367b6eb-78d9-4584-846d-4dcd7cf0bf6f · outbound

This paper cites Where and how to transfer: Knowledge aggregation-induced transferability perception for unsupervised domain adaptation,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Where and how to transfer: Knowledge aggregation-induced transferability perception for unsupervised domain adaptation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.797538Z

Source-reported events for the cited work

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Observation 8c4c1a71-a6c8-4b8e-a05d-bdeb7ee60787 · outbound

This paper cites Shape-aware meta-learning for generalizing prostate mri segmentation to unseen domains,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Shape-aware meta-learning for generalizing prostate mri segmentation to unseen domains,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.786448Z

Source-reported events for the cited work

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Observation f0fab988-97cc-4c61-a123-7398ab6c389a · outbound

This paper cites Causality-inspired single-source domain generalization for medical image segmentation,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Causality-inspired single-source domain generalization for medical image segmentation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.776137Z

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Observation 4348905c-de74-4010-9c3c-39c34b566ba8 · outbound

This paper cites Bayeseg: Bayesian modeling for medical image segmentation with interpretable generalizability,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Bayeseg: Bayesian modeling for medical image segmentation with interpretable generalizability,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.765558Z

Source-reported events for the cited work

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Observation 7f8c2bb7-c514-40ee-a801-4a2207108887 · outbound

This paper cites Improving cross- domain generalizability of medical image segmentation using uncer- tainty and shape-aware continual test-time domain adaptation,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Improving cross- domain generalizability of medical image segmentation using uncer- tainty and shape-aware continual test-time domain adaptation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.754443Z

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Observation 431a322f-afda-43b5-8264-8d4b16395b53 · outbound

This paper cites Synthseg: Segmentation of brain mri scans of any contrast and resolution without retraining,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Synthseg: Segmentation of brain mri scans of any contrast and resolution without retraining,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.743822Z

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Observation 54f79730-afe0-4bec-9198-108827e2c791 · outbound

This paper cites Randstainna: Learning stain- agnostic features from histology slides by bridging stain augmentation and normalization,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Randstainna: Learning stain- agnostic features from histology slides by bridging stain augmentation and normalization,

Reference 16

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Observation a05de0ac-ba3b-4290-86a4-e014c61baa2d · outbound

This paper cites Test-time image- to-image translation ensembling improves out-of-distribution generaliza- tion in histopathology,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Test-time image- to-image translation ensembling improves out-of-distribution generaliza- tion in histopathology,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.722923Z

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Observation 52d6ea94-d0f4-439e-abd3-4a997e221085 · outbound

This paper cites Mutual information-based disentangled neural net- works for classifying unseen categories in different domains: Application to fetal ultrasound imaging,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Mutual information-based disentangled neural net- works for classifying unseen categories in different domains: Application to fetal ultrasound imaging,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.712804Z

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Observation 52445077-8de5-4aaf-bbaf-49040878142f · outbound

This paper cites Mi- segnet: Mutual information-based us segmentation for unseen domain generalization,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Mi- segnet: Mutual information-based us segmentation for unseen domain generalization,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.702591Z

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Observation d6329eb6-6852-4470-9ed6-e73dc8998d34 · outbound

This paper cites Generalizable cross- modality medical image segmentation via style augmentation and dual normalization,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Generalizable cross- modality medical image segmentation via style augmentation and dual normalization,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.692108Z

Source-reported events for the cited work

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Observation 990acdf3-beae-4a32-8fab-c60fdee5f7d5 · outbound

This paper cites Treasure in distribution: A domain randomization based multi-source domain generalization for 2d medical image segmentation,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Treasure in distribution: A domain randomization based multi-source domain generalization for 2d medical image segmentation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.680913Z

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Observation c936fcfd-292f-482c-a731-d1be1c03d18a · outbound

This paper cites Domain generalization with correlated style uncertainty,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Domain generalization with correlated style uncertainty,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.670463Z

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Observation eb44a60c-0ef0-490d-815e-54df7f65b3a0 · outbound

This paper cites Deep feature interpolation for image content changes,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Deep feature interpolation for image content changes,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.659999Z

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Observation cb39b905-ecd7-4c24-b254-1903db24f2f1 · outbound

This paper cites Regularizing deep networks with semantic data augmentation,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Regularizing deep networks with semantic data augmentation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.649126Z

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Observation dfb9217c-e4f4-4fc3-bb22-0356bee24c10 · outbound

This paper cites Bsda: Bayesian random semantic data augmentation for medical image classification,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Bsda: Bayesian random semantic data augmentation for medical image classification,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.638003Z

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Observation 39ce471b-e061-4a44-8a47-e37ff7f4756a · outbound

This paper cites Dofe: Domain-oriented feature embedding for generalizable fundus image seg- mentation on unseen datasets,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Dofe: Domain-oriented feature embedding for generalizable fundus image seg- mentation on unseen datasets,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.626046Z

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Observation c3d741f4-a3c9-4794-82d2-339843393746 · outbound

This paper cites Learning generalized medical image segmentation from decoupled feature queries,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Learning generalized medical image segmentation from decoupled feature queries,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.614740Z

Source-reported events for the cited work

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Observation b20cb86f-2e28-4714-91ff-a7f168e9da1b · outbound

This paper cites A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.603963Z

Source-reported events for the cited work

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Observation dae38b0a-c50a-4819-a22d-466eef1b2849 · outbound

This paper cites Rim-one: An open retinal image database for optic nerve evaluation,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Rim-one: An open retinal image database for optic nerve evaluation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.592731Z

Source-reported events for the cited work

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Observation f93fa6df-4cf1-44de-9f71-dfc34a87ce4b · outbound

This paper cites Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:56:51.580946Z

Source-reported events for the cited work

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Observation 58bec9ed-b17e-41b6-825e-fc87a4851b73 · outbound

This paper cites Domain Generalization with MixStyle.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Domain Generalization with MixStyle

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T10:56:51.515841Z

Source-reported events for the cited work

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Observation 78f41c42-46ea-4d33-9555-0232c3e9f73e · outbound

This paper cites Exact feature distribution matching for arbitrary style transfer and domain generalization,.

Learning Semantic Directions for Feature Augmentation in Domain-Generalized Medical Segmentation Exact feature distribution matching for arbitrary style transfer and domain generalization,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T10:56:51.519688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

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