Paper Citation Record · LEDGER

Diffusion-Based Data Augmentation for Medical Image Segmentation

As of 7 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2508.17844.

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

pith.paper-citation-record.v1
2508.17844 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:49:44.934286Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

42 of 42 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5b7fb880-703f-4093-8475-e34a655fb765 · outbound

This paper cites Latent space synergy: Text-guided data aug- mentation for direct diffusion biomedical segmentation.

Diffusion-Based Data Augmentation for Medical Image Segmentation Latent space synergy: Text-guided data aug- mentation for direct diffusion biomedical segmentation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:58.308725Z

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Observation 5132ab3a-4cc2-42b4-abc9-68aad1e38c8c · outbound

This paper cites Towards real unsupervised anomaly de- tection via confident meta-learning.

Diffusion-Based Data Augmentation for Medical Image Segmentation Towards real unsupervised anomaly de- tection via confident meta-learning

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:58.004756Z

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Observation b0b99515-f281-4996-a8a5-80c3f6504c04 · outbound

This paper cites Wasserstein generative adversarial networks.

Diffusion-Based Data Augmentation for Medical Image Segmentation Wasserstein generative adversarial networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:57.720214Z

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Observation b7c6c939-cffe-4b45-8c44-785c3cab7572 · outbound

This paper cites Autoencoders for unsuper- vised anomaly segmentation in brain mr images: a compara- tive study.

Diffusion-Based Data Augmentation for Medical Image Segmentation Autoencoders for unsuper- vised anomaly segmentation in brain mr images: a compara- tive study

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:57.540419Z

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Observation 73c166c4-b492-475c-af97-f90e7f6a5f3c · outbound

This paper cites Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs.

Diffusion-Based Data Augmentation for Medical Image Segmentation Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:57.113287Z

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Observation 9c4339d6-3c1c-4151-9eba-27975cc38b31 · outbound

This paper cites Clinical-grade computational pathology using weakly supervised deep learning on whole slide images.

Diffusion-Based Data Augmentation for Medical Image Segmentation Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Reference 6

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Observation 0347d435-fb6f-40ab-9e30-1333c79ee8f1 · outbound

This paper cites RoentGen: Vision-Language Foundation Model for Chest X-ray Generation.

Diffusion-Based Data Augmentation for Medical Image Segmentation RoentGen: Vision-Language Foundation Model for Chest X-ray Generation

Reference 7

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Source-reported events for the cited work

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Observation 2f85c7f5-9c00-47fe-9156-4139322b3385 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Diffusion-Based Data Augmentation for Medical Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 8

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Observation 7b69f624-ae41-4653-b6bc-d2318ade2f0f · outbound

This paper cites Diffusion trans- former u-net for medical image segmentation.

Diffusion-Based Data Augmentation for Medical Image Segmentation Diffusion trans- former u-net for medical image segmentation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:56.207773Z

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Observation a82bd498-69a1-43fa-8b15-3a97ff226ef6 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Diffusion-Based Data Augmentation for Medical Image Segmentation Diffusion models beat gans on image synthesis

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:55.784760Z

Source-reported events for the cited work

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Observation 6a1329c3-c45f-4ff0-a3f7-4e6a1de01b1f · outbound

This paper cites Deep learning-enabled medical com- puter vision.

Diffusion-Based Data Augmentation for Medical Image Segmentation Deep learning-enabled medical com- puter vision

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:55.474751Z

Source-reported events for the cited work

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Observation 506cb643-8172-43f1-b148-4106c7d184a8 · outbound

This paper cites Pranet: Parallel reverse attention network for polyp segmentation.

Diffusion-Based Data Augmentation for Medical Image Segmentation Pranet: Parallel reverse attention network for polyp segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:55.182180Z

Source-reported events for the cited work

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Observation 029c5063-1dea-4f42-9021-38e420621e72 · outbound

This paper cites Joint optic disc and cup seg- mentation based on multi-label deep network and polar trans- formation.

Diffusion-Based Data Augmentation for Medical Image Segmentation Joint optic disc and cup seg- mentation based on multi-label deep network and polar trans- formation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:54.984795Z

Source-reported events for the cited work

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Observation 738cf455-1d04-4ab0-82ad-7d5b5447ea3f · outbound

This paper cites Robust compressed sensing mri with deep generative priors.

Diffusion-Based Data Augmentation for Medical Image Segmentation Robust compressed sensing mri with deep generative priors

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:54.761989Z

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Observation ef85bea3-9e0d-4561-9cce-ab15a3c51c9c · outbound

This paper cites Kvasir-seg: A segmented polyp dataset.

Diffusion-Based Data Augmentation for Medical Image Segmentation Kvasir-seg: A segmented polyp dataset

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:54.443058Z

Source-reported events for the cited work

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Observation f9e2de53-7b0d-47c8-8122-be56c96aab32 · outbound

This paper cites Survey on deep learning with class imbalance.

Diffusion-Based Data Augmentation for Medical Image Segmentation Survey on deep learning with class imbalance

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:53.606901Z

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Observation fc938dc9-20d9-4bb0-98a6-3b5dc2299df0 · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

Diffusion-Based Data Augmentation for Medical Image Segmentation A style-based generator architecture for generative adversarial networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:53.368892Z

Source-reported events for the cited work

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Observation a2c6dc69-8a24-47e5-8c9d-44b2dee845d4 · outbound

This paper cites Gans for medical image analysis.

Diffusion-Based Data Augmentation for Medical Image Segmentation Gans for medical image analysis

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:52.984846Z

Source-reported events for the cited work

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Observation a8bb678f-825e-42d2-b8e1-6b0a6be0727d · outbound

This paper cites Diffusion models in medical imaging: A comprehensive survey.

Diffusion-Based Data Augmentation for Medical Image Segmentation Diffusion models in medical imaging: A comprehensive survey

Reference 19

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Observation 21a22ef8-fe8a-4570-ac30-33c530f9e683 · outbound

This paper cites Auto-encoding vari- ational bayes, 2013.

Diffusion-Based Data Augmentation for Medical Image Segmentation Auto-encoding vari- ational bayes, 2013

Reference 20

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Source-reported events for the cited work

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Observation f34f5339-459d-412e-b260-4805289f1418 · outbound

This paper cites Stable diffusion segmentation for biomed- ical images with single-step reverse process.

Diffusion-Based Data Augmentation for Medical Image Segmentation Stable diffusion segmentation for biomed- ical images with single-step reverse process

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:52.244752Z

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Observation b2e5adaa-3765-43c9-923f-1bd7974869d9 · outbound

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

Diffusion-Based Data Augmentation for Medical Image Segmentation A survey on deep learning in medical image analysis

Reference 22

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Source-reported events for the cited work

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Observation 47ef669e-2923-4461-b96f-bf8568874880 · outbound

This paper cites A multimodal comparison of latent denois- ing diffusion probabilistic models and generative adversarial networks for medical image synthesis.

Diffusion-Based Data Augmentation for Medical Image Segmentation A multimodal comparison of latent denois- ing diffusion probabilistic models and generative adversarial networks for medical image synthesis

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 26cceaaf-907f-4b81-b73b-1d52ca1d3c85 · outbound

This paper cites Refuge challenge: A unified framework for evaluat- ing automated methods for glaucoma assessment from fun- dus photographs.

Diffusion-Based Data Augmentation for Medical Image Segmentation Refuge challenge: A unified framework for evaluat- ing automated methods for glaucoma assessment from fun- dus photographs

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:51.546719Z

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Observation ea0bbb1e-aad5-470e-a016-4119ad2bf27e · outbound

This paper cites Deep structural causal models for tractable counterfactual in- ference.

Diffusion-Based Data Augmentation for Medical Image Segmentation Deep structural causal models for tractable counterfactual in- ference

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:51.048732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f13f431a-103c-4482-b7c3-83e4e8882c8b · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Diffusion-Based Data Augmentation for Medical Image Segmentation SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 26

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Observation 983d0842-dcfe-4198-87f4-6012a2a645eb · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Diffusion-Based Data Augmentation for Medical Image Segmentation High-resolution image synthesis with latent diffusion models

Reference 27

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Observation df162be0-412d-48eb-8b8e-e64ab57bce68 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

Diffusion-Based Data Augmentation for Medical Image Segmentation U- net: Convolutional networks for biomedical image segmen- tation

Reference 28

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Observation 179fbf08-6c24-4524-bc0d-ffbf89cba5d6 · outbound

This paper cites Waldstein, Ursula Schmidt-Erfurth, and Georg Langs.

Diffusion-Based Data Augmentation for Medical Image Segmentation Waldstein, Ursula Schmidt-Erfurth, and Georg Langs

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:50.167014Z

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Observation 45946602-68a1-41ce-b3b3-682fdf73b57e · outbound

This paper cites Natural synthetic anomalies for self-supervised anomaly detection and localization.

Diffusion-Based Data Augmentation for Medical Image Segmentation Natural synthetic anomalies for self-supervised anomaly detection and localization

Reference 30

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Observation debfd30c-800d-4d91-8310-5548d0ec6056 · outbound

This paper cites MadCLIP: Few-shot Medical Anomaly Detection with CLIP.

Diffusion-Based Data Augmentation for Medical Image Segmentation MadCLIP: Few-shot Medical Anomaly Detection with CLIP

Reference 31

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Observation 54dcdb34-7f25-4a1e-95e8-9744b65f83c6 · outbound

This paper cites A survey on image data augmentation for deep learning.

Diffusion-Based Data Augmentation for Medical Image Segmentation A survey on image data augmentation for deep learning

Reference 32

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Source-reported events for the cited work

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Observation 881024ca-52cd-49ad-b368-fc67de3347f2 · outbound

This paper cites Toward embedded detection of polyps in wce images for early diagnosis of colorectal can- cer.

Diffusion-Based Data Augmentation for Medical Image Segmentation Toward embedded detection of polyps in wce images for early diagnosis of colorectal can- cer

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:49.254826Z

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Observation 7ac38963-a3f3-463c-b804-fd878bbf06c4 · outbound

This paper cites Stepwise feature fusion: Local guides global.

Diffusion-Based Data Augmentation for Medical Image Segmentation Stepwise feature fusion: Local guides global

Reference 34

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Source-reported events for the cited work

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Observation 0ade23fd-9909-4c16-aa6b-221d8a300b84 · outbound

This paper cites Preparing medical imaging data for machine learning.

Diffusion-Based Data Augmentation for Medical Image Segmentation Preparing medical imaging data for machine learning

Reference 35

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Observation 998361ea-62fd-4dd1-9836-40d1d9bd1f04 · outbound

This paper cites Diffusion models for implicit image segmentation ensembles.

Diffusion-Based Data Augmentation for Medical Image Segmentation Diffusion models for implicit image segmentation ensembles

Reference 36

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Source-reported events for the cited work

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Observation a4a84160-247d-4e11-99e6-d7ffa5117cd0 · outbound

This paper cites Medsegdiff: Medical image segmentation with diffusion probabilistic model.

Diffusion-Based Data Augmentation for Medical Image Segmentation Medsegdiff: Medical image segmentation with diffusion probabilistic model

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:47.284756Z

Source-reported events for the cited work

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Observation be36d50b-510c-448b-aad2-f91e5f8936a6 · outbound

This paper cites Medsegdiff-v2: Diffusion-based medical im- age segmentation with transformer.

Diffusion-Based Data Augmentation for Medical Image Segmentation Medsegdiff-v2: Diffusion-based medical im- age segmentation with transformer

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:46.966087Z

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Observation fb487fdd-abc9-412b-a855-e5b4d3ad2d36 · outbound

This paper cites Generative adversar- ial network in medical imaging: A review.

Diffusion-Based Data Augmentation for Medical Image Segmentation Generative adversar- ial network in medical imaging: A review

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:46.501529Z

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Observation 0031ad21-0d48-4a32-bd6d-cd3eec05c554 · outbound

This paper cites Medical visual question answering via conditional rea- soning.

Diffusion-Based Data Augmentation for Medical Image Segmentation Medical visual question answering via conditional rea- soning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:49:46.043634Z

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Observation 1dd0dad4-b4aa-4a0f-93d4-3ecbe069ba8e · outbound

This paper cites BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs.

Diffusion-Based Data Augmentation for Medical Image Segmentation BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

Reference 41

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This paper cites an unresolved cited work.

Diffusion-Based Data Augmentation for Medical Image Segmentation Unresolved cited work

Reference 462

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Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.