Paper Citation Record · LEDGER

Comparative Analysis of Diffusion Generative Models in Computational Pathology

As of 15 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2411.15719.

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

pith.paper-citation-record.v1
2411.15719 v1

Coverage vector

measured 41 of 41 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-12T14:02:42.442917Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 6fa3a13c-7931-4dc6-83bc-14182c8a5a4d · outbound

This paper cites Digital pathology: advantages, limitations and emerging perspectives.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Digital pathology: advantages, limitations and emerging perspectives

Reference 1

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Observation c1cde2b7-c632-42b1-a7b0-b1a9fadf524a · outbound

This paper cites Dig- ital pathology and artificial intelligence in translational medicine and clinical practice.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Dig- ital pathology and artificial intelligence in translational medicine and clinical practice

Reference 2

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Observation 2cd70147-b99b-42d8-89b1-d51b458467ca · outbound

This paper cites Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases

Reference 3

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Observation 98a2c3aa-8364-4876-8374-40474f381364 · outbound

This paper cites Deep learning in histopathology: the path to the clinic.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Deep learning in histopathology: the path to the clinic

Reference 4

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Observation 8ee7e2b3-89a6-4e9c-8eb5-a9438dff4e98 · outbound

This paper cites Deep learning in cancer pathology: a new generation of clinical biomarkers.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Deep learning in cancer pathology: a new generation of clinical biomarkers

Reference 5

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Observation ace79fe6-ef6c-44e7-aea1-483b5ddc0049 · outbound

This paper cites How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?.

Comparative Analysis of Diffusion Generative Models in Computational Pathology How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?

Reference 6

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Observation fa358aa5-0557-4e57-97c8-642fb1931ee5 · outbound

This paper cites Privacy in the age of medical big data.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Privacy in the age of medical big data

Reference 7

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Observation 9a06d4d1-50ff-4001-ac81-0361d067d152 · outbound

This paper cites Between generating noise and generating images: Noise in the correct frequency improves the quality of synthetic histopathology images for digital pathology.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Between generating noise and generating images: Noise in the correct frequency improves the quality of synthetic histopathology images for digital pathology

Reference 8

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Observation 3cbf81d1-7fe6-4d3b-adb6-e98a1874078f · outbound

This paper cites Computational pathology: a sur- vey review and the way forward.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Computational pathology: a sur- vey review and the way forward

Reference 9

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Observation 327fc63e-35e9-48e2-b0e6-311124751a47 · outbound

This paper cites Evaluation of the use of single-and multi- magnification convolutional neural networks for the determination and quantitation of lesions in nonclinical pathology studies.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Evaluation of the use of single-and multi- magnification convolutional neural networks for the determination and quantitation of lesions in nonclinical pathology studies

Reference 10

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Observation 0ecb47c5-e162-4819-9a53-d9504f2ac033 · outbound

This paper cites Synthetic data in machine learning for medicine and healthcare.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Synthetic data in machine learning for medicine and healthcare

Reference 11

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Observation cabc9173-8f22-400b-884b-80cad3cb8a36 · outbound

This paper cites Gen- erative adversarial networks.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Gen- erative adversarial networks

Reference 12

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Observation f41d1b45-b409-4742-a7c8-3a589fefe6f8 · outbound

This paper cites A disentangled generative model for disease decomposition in chest x-rays via normal image synthesis.

Comparative Analysis of Diffusion Generative Models in Computational Pathology A disentangled generative model for disease decomposition in chest x-rays via normal image synthesis

Reference 13

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Observation f0bf1008-894f-4406-a2a9-c854b64ee9e7 · outbound

This paper cites Hi- net: hybrid-fusion network for multi-modal mr image synthesis.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Hi- net: hybrid-fusion network for multi-modal mr image synthesis

Reference 14

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Observation e04cb939-5de3-49b9-8202-68756e06414b · outbound

This paper cites PathologyGAN: Learning deep representations of cancer tissue.

Comparative Analysis of Diffusion Generative Models in Computational Pathology PathologyGAN: Learning deep representations of cancer tissue

Reference 15

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Observation e544eb3a-b8bd-4e42-8e6d-498cdd2176ef · outbound

This paper cites Deep semi supervised generative learning for automated tumor proportion scoring on nsclc tissue needle biopsies.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Deep semi supervised generative learning for automated tumor proportion scoring on nsclc tissue needle biopsies

Reference 16

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Observation a3e8e0df-7c9f-4682-8cb9-429f021446d0 · outbound

This paper cites Deepfake histologic images for enhancing digital pathology.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Deepfake histologic images for enhancing digital pathology

Reference 17

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Observation 126248a9-b98d-40cc-a638-a91c895fae76 · outbound

This paper cites Denoising diffusion prob- abilistic models.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Denoising diffusion prob- abilistic models

Reference 18

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Observation 9d22dcce-664e-4cb6-8f2d-1b5613e387bc · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Generative modeling by estimating gradients of the data distribution

Reference 19

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Observation 4d7d1ad2-94d9-4f7e-bd18-c72fe0bad526 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Score-Based Generative Modeling through Stochastic Differential Equations

Reference 20

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Observation a021a709-2b1e-4dee-a76b-ae7e34cbeea0 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Diffusion models beat gans on image synthesis

Reference 21

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Observation 96d3af8a-0422-48b1-950e-5e407a45720f · outbound

This paper cites Improved denoising diffusion probabilistic models.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Improved denoising diffusion probabilistic models

Reference 22

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Observation 5881b894-b401-4142-ab4d-ed8fdf709387 · outbound

This paper cites Improved techniques for training score- based generative models.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Improved techniques for training score- based generative models

Reference 23

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Observation f314fe96-e16a-4064-be9b-336d8cf66a49 · outbound

This paper cites Srdiff: Single image super-resolution with diffusion probabilistic models.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Srdiff: Single image super-resolution with diffusion probabilistic models

Reference 24

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Observation f641010f-5100-4cc4-9dd0-5d9f13e82aca · outbound

This paper cites Diffmix: Diffusion model-based data synthesis for nuclei segmentation and classification in imbalanced pathology image datasets.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Diffmix: Diffusion model-based data synthesis for nuclei segmentation and classification in imbalanced pathology image datasets

Reference 25

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Observation 742f6574-8b4c-482b-8de5-baa80e24b075 · outbound

This paper cites Card: Classifica- tion and regression diffusion models.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Card: Classifica- tion and regression diffusion models

Reference 26

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Observation b6a7dd0c-4e29-4e53-8f0c-84e4869dfe5b · outbound

This paper cites Pathldm: Text conditioned la- tent diffusion model for histopathology.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Pathldm: Text conditioned la- tent diffusion model for histopathology

Reference 27

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Observation 83648b53-bb89-4edb-b278-207beb4a7098 · outbound

This paper cites A morphology focused diffusion probabilistic model for synthesis of histopathology images.

Comparative Analysis of Diffusion Generative Models in Computational Pathology A morphology focused diffusion probabilistic model for synthesis of histopathology images

Reference 28

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Observation e63e484d-48fe-4361-905b-3535866e2e08 · outbound

This paper cites Learned representation-guided diffusion models for large-image generation.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Learned representation-guided diffusion models for large-image generation

Reference 29

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Observation 633634b2-2da6-445e-a9bb-09fecac66774 · outbound

This paper cites A boosted classifier for integrating multiple fields of view: Breast can- cer grading in histopathology.

Comparative Analysis of Diffusion Generative Models in Computational Pathology A boosted classifier for integrating multiple fields of view: Breast can- cer grading in histopathology

Reference 30

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This paper cites Deep unsupervised learning using nonequilibrium thermody- namics.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Deep unsupervised learning using nonequilibrium thermody- namics

Reference 31

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Observation 10abff81-f5f9-4448-92b5-e77c8e9e688b · outbound

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

Comparative Analysis of Diffusion Generative Models in Computational Pathology High-resolution image synthesis with latent diffusion models

Reference 32

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Observation 5038cde5-72b1-46f8-b217-23a7b7730199 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Classifier-Free Diffusion Guidance

Reference 33

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This paper cites Elucidating the Exposure Bias in Diffusion Models.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Elucidating the Exposure Bias in Diffusion Models

Reference 34

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This paper cites Neural discrete representation learning.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Neural discrete representation learning

Reference 35

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This paper cites Improved techniques for training gans.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Improved techniques for training gans

Reference 36

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This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 37

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Observation 30970365-a1ac-4b15-8e3e-0f66fae67fdc · outbound

This paper cites Going deeper with convolutions.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Going deeper with convolutions

Reference 38

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This paper cites Demystifying MMD GANs.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Demystifying MMD GANs

Reference 39

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Observation 663e8af9-d39d-48b4-b2a7-76a0e39d01c0 · outbound

This paper cites Deep resid- ual learning for image recognition.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Deep resid- ual learning for image recognition

Reference 40

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Observation 84568d01-0987-49dd-8fad-56d4310a9971 · outbound

This paper cites Generating synthetic data in digital pathology through diffusion models: a multi- faceted approach to evaluation.

Comparative Analysis of Diffusion Generative Models in Computational Pathology Generating synthetic data in digital pathology through diffusion models: a multi- faceted approach to evaluation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:02:42.518853Z

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Pith citing papers

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