Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T21:56:51.758281Z
Paper Citation Record · LEDGER
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation
As of 23 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2501.03466.
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 2026-08-23T06:30:58.430688+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
65 of 65 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 87827b49-195c-4c93-9cde-434993af769c · outbound
This paper cites Retinal imaging and image analysis,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Retinal imaging and image analysis,
Reference 1
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 7b578236-529b-4435-beec-12cd17c24387 · outbound
This paper cites Large-scale retrieval for medical image analytics: A comprehensive review,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Large-scale retrieval for medical image analytics: A comprehensive review,
Reference 2
- Resolution
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Source-reported events for the cited work
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Observation 86f41df4-8e10-49fa-a4fd-5e92aa71734f · outbound
This paper cites Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs,
Reference 3
- Resolution
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Source-reported events for the cited work
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Observation 7437a61e-3743-41ce-864b-71a3ccbc43f7 · outbound
This paper cites Idrid: Diabetic retinopathy–segmentation and grading challenge,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Idrid: Diabetic retinopathy–segmentation and grading challenge,
Reference 4
- Resolution
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Source-reported events for the cited work
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Observation 506cbf0c-7df3-4159-b938-4c371ea1ff46 · outbound
This paper cites Domain adaptation for medical image analysis: a survey,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Domain adaptation for medical image analysis: a survey,
Reference 5
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Source-reported events for the cited work
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Observation 8852763b-f8de-44e9-a60e-d1ec13d874f0 · outbound
This paper cites Domain-adversarial training of neural networks,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Domain-adversarial training of neural networks,
Reference 6
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Source-reported events for the cited work
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Observation f90d8f59-5f1d-4186-a0c3-dd1cf5f133d8 · outbound
This paper cites Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Unsupervised domain adaptation in brain lesion segmentation with adversarial networks,
Reference 7
- Resolution
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Source-reported events for the cited work
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Observation 559c1bac-6d55-4677-862b-c4f828743b6c · outbound
This paper cites Patch -based output space adversarial learning for joint optic disc and cup segmentation,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Patch -based output space adversarial learning for joint optic disc and cup segmentation,
Reference 8
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 943cacfa-97ff-4280-8d5b-803a9a5fdbc5 · outbound
This paper cites Domain generalization via invariant feature representation,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Domain generalization via invariant feature representation,
Reference 9
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Source-reported events for the cited work
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Observation 297de1f6-74bf-4721-85b4-af232e74e958 · outbound
This paper cites Domain generalization: A survey,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Domain generalization: A survey,
Reference 10
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 4bec5c5c-574a-45ca-be50-2c6fe4907e2d · outbound
This paper cites A comprehensive survey on transfer learning,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation A comprehensive survey on transfer learning,
Reference 11
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Source-reported events for the cited work
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Observation b2057539-7130-462d-9a1e-7b27181061d2 · outbound
This paper cites Unsupervised domain adaptation in semantic segmentation: a review,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Unsupervised domain adaptation in semantic segmentation: a review,
Reference 12
- Resolution
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Source-reported events for the cited work
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Observation 96f30238-9615-49e7-9a3e-ff390a293380 · outbound
This paper cites Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning,
Reference 13
- Resolution
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Source-reported events for the cited work
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Observation 48dd2b97-77ea-44c3-a28b-4ba238eeb44c · outbound
This paper cites Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation,
Reference 14
- Resolution
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Source-reported events for the cited work
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Observation ffa28089-d3a7-4359-900c-4f3d5b315d84 · outbound
This paper cites Source-free unsupervised adaptive segmentation for knee joint MRI,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Source-free unsupervised adaptive segmentation for knee joint MRI,
Reference 15
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Source-reported events for the cited work
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Observation 27e8a4f3-ec26-4d73-a647-37da8b272639 · outbound
This paper cites Domain generalization via model-agnostic learning of semantic features,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Domain generalization via model-agnostic learning of semantic features,
Reference 16
- Resolution
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Source-reported events for the cited work
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Observation 362882bd-a202-48eb-9477-4f239321b0b3 · outbound
This paper cites Generalizing to unseen domains: A survey on domain generalization,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Generalizing to unseen domains: A survey on domain generalization,
Reference 17
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Source-reported events for the cited work
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Observation ed1aa90c-e62d-4418-a79e-ecf5e32b8c42 · outbound
This paper cites Domain Generalization for Medical Image Analysis: A Review.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Domain Generalization for Medical Image Analysis: A Review
Reference 18
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Source-reported events for the cited work
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Observation f2263032-26ad-4a74-ad6c-3627660d0bef · outbound
This paper cites Dofe: Domain-oriented feature embedding for generalizable fundus image segmentation on unseen datasets,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Dofe: Domain-oriented feature embedding for generalizable fundus image segmentation on unseen datasets,
Reference 19
- Resolution
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Source-reported events for the cited work
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Observation 96f8ac3b-ed81-454e-92dd-ea59269a2e95 · outbound
This paper cites Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space,
Reference 20
- Resolution
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Source-reported events for the cited work
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Observation 38dde893-109e-4e43-ac63-1a0f9778728d · outbound
This paper cites AADG: Automatic augmentation for domain generalization on retinal image segmentation,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation AADG: Automatic augmentation for domain generalization on retinal image segmentation,
Reference 21
- Resolution
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Source-reported events for the cited work
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Observation 13e2fb6e-92c9-4ccd-bec7-0507fef9ff9d · outbound
This paper cites Retinal arteriolar narrowing and risk of coronary heart disease in men and women: the Atherosclerosis Risk in Communities Study,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Retinal arteriolar narrowing and risk of coronary heart disease in men and women: the Atherosclerosis Risk in Communities Study,
Reference 22
- Resolution
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Source-reported events for the cited work
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Observation ce8c6e9b-26ec-4d03-b172-f9433bd3dfb3 · outbound
This paper cites Impact of current and past blood pressure on retinal arteriolar diameter in an older population,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Impact of current and past blood pressure on retinal arteriolar diameter in an older population,
Reference 23
- Resolution
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Source-reported events for the cited work
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Observation a6fc787a-0790-414d-afe2-7af5ee115bf0 · outbound
This paper cites Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures,
Reference 24
- Resolution
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Source-reported events for the cited work
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Observation 9765f5fc-1d5b-4c4e-8823-3ac8c1bef0fa · outbound
This paper cites Segmentation of retinal blood vessels by combining the detection of centerlines and morpholo gical reconstruction,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Segmentation of retinal blood vessels by combining the detection of centerlines and morpholo gical reconstruction,
Reference 25
- Resolution
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Source-reported events for the cited work
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Observation eff35647-0461-4e33-a0bd-d3f90b8dd3f0 · outbound
This paper cites Detection of blood vessels in retinal images using two-dimensional matched filters,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Detection of blood vessels in retinal images using two-dimensional matched filters,
Reference 26
- Resolution
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Source-reported events for the cited work
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Observation 1ae4e78e-3706-4ba2-aed3-5949957a6699 · outbound
This paper cites Segmentation of vessel -like patterns using mathematical morphology and curvature evaluation,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Segmentation of vessel -like patterns using mathematical morphology and curvature evaluation,
Reference 27
- Resolution
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Source-reported events for the cited work
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Observation 6616747f-7872-4109-8adf-8469d250d146 · outbound
This paper cites U-net: Convolutional networks for biomedical image segmentation,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation U-net: Convolutional networks for biomedical image segmentation,
Reference 28
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 876c1863-b199-4b4d-b1d6-d2c3b7a4070c · outbound
This paper cites Ce-net: Context encoder network for 2d medical image segmentation,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Ce-net: Context encoder network for 2d medical image segmentation,
Reference 29
- Resolution
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Source-reported events for the cited work
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Observation 1be50c7f-6b0a-450d-93c4-00707ab0c022 · outbound
This paper cites Unet++: Redesigning skip connections to exploit multiscale features in image segmentation,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Unet++: Redesigning skip connections to exploit multiscale features in image segmentation,
Reference 30
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 480eeca9-2909-4670-b999-6264a7067088 · outbound
This paper cites CS-Net: Channel and spatial attention network for curvilinear structure segmentation,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation CS-Net: Channel and spatial attention network for curvilinear structure segmentation,
Reference 31
- Resolution
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Source-reported events for the cited work
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Observation 7e773dba-2f1e-42c2-87bb-f2bd476afb1a · outbound
This paper cites Retinal vessel segmentation using deep learning: a review,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Retinal vessel segmentation using deep learning: a review,
Reference 32
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 25b981e3-3e1d-4519-a235-845e366ba96a · outbound
This paper cites Virtual adversarial training: a regularization method for supervised and semi-supervised learning,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Virtual adversarial training: a regularization method for supervised and semi-supervised learning,
Reference 33
- Resolution
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Source-reported events for the cited work
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Observation c443654d-4a45-421c-a32e-961a7d8f1c0b · outbound
This paper cites AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Reference 34
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Source-reported events for the cited work
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Observation c6429161-96b2-431c-bfa8-68607d279c7f · outbound
This paper cites Augmax: Adversarial composition of random augmentations fo r robust training,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Augmax: Adversarial composition of random augmentations fo r robust training,
Reference 35
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 7b7da31b-5b8e-4fd1-b243-6e2870241cd4 · outbound
This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation The many faces of robustness: A critical analysis of out-of-distribution generalization,
Reference 36
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Source-reported events for the cited work
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Observation b5aa94e9-a61d-4abd-8954-509f4a4e0863 · outbound
This paper cites AutoAugment: Learning Augmentation Policies from Data.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation AutoAugment: Learning Augmentation Policies from Data
Reference 37
- Resolution
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Source-reported events for the cited work
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Observation ac2e8a87-7aa7-42bd-9924-1b2600dcfa25 · outbound
This paper cites Improving robustness against common corruptions with frequency biased models,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Improving robustness against common corruptions with frequency biased models,
Reference 38
- Resolution
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Source-reported events for the cited work
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Observation 51d7e88d-0957-4fdd-a86c-08a4430a7cc9 · outbound
This paper cites Prime: A few primitives can boost robustness to common corruptions,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Prime: A few primitives can boost robustness to common corruptions,
Reference 39
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ff769987-bf20-4fd3-b327-349c70ac7afe · outbound
This paper cites Amplitude-phase recombination: Rethinking robustness of convolutional neural networks in frequency domain,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Amplitude-phase recombination: Rethinking robustness of convolutional neural networks in frequency domain,
Reference 40
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 846fe02b-dee2-477f-9edf-b1e39bdb43b6 · outbound
This paper cites Pixmix: Dreamlike pictures comprehensively improve safety measures,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Pixmix: Dreamlike pictures comprehensively improve safety measures,
Reference 41
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 12687466-d9e9-4f05-8bc8-b68b9d1aad89 · outbound
This paper cites Generative adversarial networks,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Generative adversarial networks,
Reference 42
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation e49dbb19-243b-4552-b04e-a40b6e23f69a · outbound
This paper cites DR -GAN: conditional generative adversarial network for fine -grained lesion synthesis on diabetic retinopathy images,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation DR -GAN: conditional generative adversarial network for fine -grained lesion synthesis on diabetic retinopathy images,
Reference 43
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 3d9f64a2-25ab-4f5e-b564-a114a6d4aa78 · outbound
This paper cites Unsupervised domain adaptation for cross -modality retinal vessel segmentation via disentangling representation style transfer and collaborative consistency learning,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Unsupervised domain adaptation for cross -modality retinal vessel segmentation via disentangling representation style transfer and collaborative consistency learning,
Reference 44
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 1729ca0a-3fe5-489c-a519-07f608273b92 · outbound
This paper cites I-secret: Importance-guided fundus image enhancement via semi-supervised contrastive constraining,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation I-secret: Importance-guided fundus image enhancement via semi-supervised contrastive constraining,
Reference 45
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation b6f9bb19-1aa1-4b9c-a863-0d011e274667 · outbound
This paper cites Missing MRI pulse sequence synthesis using multi -modal generative adversarial network,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Missing MRI pulse sequence synthesis using multi -modal generative adversarial network,
Reference 46
- Resolution
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Source-reported events for the cited work
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Observation 9ca980a6-40c1-49e2-9b91-83bcd0106d2a · outbound
This paper cites Towards Adversarial Retinal Image Synthesis.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Towards Adversarial Retinal Image Synthesis
Reference 47
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3c9ad23f-a77c-4b47-96d4-6694933b7e2b · outbound
This paper cites Image-to-image translation with conditional adversarial networks,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Image-to-image translation with conditional adversarial networks,
Reference 48
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 3e5a2fc4-bac1-4630-9d5d-c1a8a90d82a4 · outbound
This paper cites End-to-end adversarial retinal image synthesis,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation End-to-end adversarial retinal image synthesis,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.227966Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 25304bf3-d723-4bca-af4e-9f4624a4e464 · outbound
This paper cites Synthetic Medical Images from Dual Generative Adversarial Networks.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Synthetic Medical Images from Dual Generative Adversarial Networks
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:56:51.697352Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 97e146d2-22f7-4c20-9667-856c5bccf9b9 · outbound
This paper cites Synthesizing retinal and neuronal images with generative adversarial nets,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Synthesizing retinal and neuronal images with generative adversarial nets,
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.214446Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation a6186ff4-1f96-4306-9452-0702109e894e · outbound
This paper cites SkrGAN: Sketching-rendering unconditional generative adversarial networks for medical image synthesis,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation SkrGAN: Sketching-rendering unconditional generative adversarial networks for medical image synthesis,
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.200398Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 2396701b-bca2-487e-8ede-b7c8684ae785 · outbound
This paper cites Yolocurvseg: You only label one noisy skeleton for vessel -style curvilinear structure segmentation,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Yolocurvseg: You only label one noisy skeleton for vessel -style curvilinear structure segmentation,
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.186931Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 6ffd6084-5dc5-41b8-8009-03f024c8e701 · outbound
This paper cites Data augmentation using learned transformations for one -shot medical image segmentation,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Data augmentation using learned transformations for one -shot medical image segmentation,
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.173148Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 30d80c0a-0c15-4e70-8fe5-56ef13e6f9b3 · outbound
This paper cites Improving the generalizability of convolutional neural network-based segmentation on CMR images,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Improving the generalizability of convolutional neural network-based segmentation on CMR images,
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.159481Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 39d07e1b-7092-451f-9ab4-020293ae3636 · outbound
This paper cites Staining invariant features for improving generalization of d eep convolutional neural networks in computational pathology,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Staining invariant features for improving generalization of d eep convolutional neural networks in computational pathology,
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.144876Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 137c9859-8236-4c9f-94b4-5e598fffb21d · outbound
This paper cites Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation,
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.130456Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 062717db-1230-410a-8ccb-050fcc8cf1e8 · outbound
This paper cites Modeling trees with a space colonization algorithm,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Modeling trees with a space colonization algorithm,
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.115950Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 647bc6b0-f081-408c-8c0e-07452e0d2d3e · outbound
This paper cites Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.100708Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ba9afd1b-0856-496b-bd6a-19a8f45fa62a · outbound
This paper cites Robust vessel segmentation in fundus images,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Robust vessel segmentation in fundus images,
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.086063Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 7426db5f-51b8-4c05-8235-b0e70a2408b0 · outbound
This paper cites Ridge -based vessel segmentation in color images of the retina,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Ridge -based vessel segmentation in color images of the retina,
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.071329Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation d97b1dfd-7231-4b91-8a90-65b5a1cc9440 · outbound
This paper cites An ensemble classification-based approach applied to retinal blood vessel segmentation,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation An ensemble classification-based approach applied to retinal blood vessel segmentation,
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.056052Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 226715ba-1bc6-4c15-ae8c-a5b34da665e9 · outbound
This paper cites Manifold mixup: Better representations by interpolating hidden states,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Manifold mixup: Better representations by interpolating hidden states,
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.040459Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 1d1b04cc-4515-465e-98fb-15b8a754959d · outbound
This paper cites Cutmix: Regularization strategy to train strong classifiers with localizable features,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Cutmix: Regularization strategy to train strong classifiers with localizable features,
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.024895Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 824279b7-8f8a-4159-b624-c3aa9568dfe8 · outbound
This paper cites Statistical comparisons of classifiers over multiple data sets,.
DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation Statistical comparisons of classifiers over multiple data sets,
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T21:56:52.009629Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Pith citing papers
No inbound Pith citation observations are available.