Paper Citation Record · LEDGER

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images

As of 10 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 5 inbound Pith citation observations for arXiv:2501.15598.

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

pith.paper-citation-record.v1
2501.15598 v1

Coverage vector

measured 27 of 27 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-10T14:10:43.739613Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

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measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:28:56.162058Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T05:25:03.894213Z

Reference resolution

27 of 27 outbound references displayed

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External citation measurements

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Outbound references

Observation 648dee04-b661-4c3c-8687-9cad3c27db25 · outbound

This paper cites AudioGen: Textually Guided Audio Generation.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images AudioGen: Textually Guided Audio Generation

Reference 4

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Observation d6399c26-8129-41ab-b7d8-37bb20ad8fcb · outbound

This paper cites Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large Language Models.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large Language Models

Reference 5

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Observation 01265537-7c29-4b7f-b98f-9a6c6c122fe9 · outbound

This paper cites Multimodal contrastive learning for spatial gene expression prediction using histology images.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images Multimodal contrastive learning for spatial gene expression prediction using histology images

Reference 6

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Observation 91537f3a-0b67-4254-b962-a63bdc54351a · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 7

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Observation 06757490-d75c-463e-8e5a-7908eb88c0c3 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images DINOv2: Learning Robust Visual Features without Supervision

Reference 8

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Observation 04794622-ffe5-41fa-9fde-89f2bb0b3406 · outbound

This paper cites Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors.BioRxiv, pp.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors.BioRxiv, pp

Reference 9

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Observation c2534a66-40c6-463b-88a4-0902b52d8217 · outbound

This paper cites Make-A-Video: Text-to-Video Generation without Text-Video Data.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images Make-A-Video: Text-to-Video Generation without Text-Video Data

Reference 10

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Observation dbc35b13-b711-42a6-a67b-6ec357ba281a · outbound

This paper cites We include the gene variation curves of each method on this dataset in Fig.10.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images We include the gene variation curves of each method on this dataset in Fig.10

Reference 11

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Observation fc8cdb35-fbe5-4253-8c86-4b8ae8a443f1 · outbound

This paper cites Virchow: A Million-Slide Digital Pathology Foundation Model.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images Virchow: A Million-Slide Digital Pathology Foundation Model

Reference 13

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Observation 3fd0b096-0e9c-492b-82f2-8d37d9e0d46f · outbound

This paper cites M2ORT: Many-To-One Regression Transformer for Spatial Transcriptomics Prediction from Histopathology Images.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images M2ORT: Many-To-One Regression Transformer for Spatial Transcriptomics Prediction from Histopathology Images

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:10:44.058863Z

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Observation a15ede40-62fe-4009-89bc-eb4183e9f97a · outbound

This paper cites SE(3) diffusion model with application to protein backbone generation.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images SE(3) diffusion model with application to protein backbone generation

Reference 15

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Observation e62ccb1c-e610-4b33-86f2-f61e8d12f0aa · outbound

This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 16

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Observation 258f4a2f-db2d-4d34-864f-850b024148a8 · outbound

This paper cites Inferring super- resolution tissue architecture by integrating spatial transcriptomics with histology.Nature biotech- nology, pp.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images Inferring super- resolution tissue architecture by integrating spatial transcriptomics with histology.Nature biotech- nology, pp

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:44.326653Z

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Observation 23103cd9-de47-4ed4-8ae4-d4fdf590446f · outbound

This paper cites Trivialized Momentum Facilitates Diffusion Generative Modeling on Lie Groups.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images Trivialized Momentum Facilitates Diffusion Generative Modeling on Lie Groups

Reference 18

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Observation 18400f0e-3b7b-4284-8ef5-ccf38de4c502 · outbound

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Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images Unresolved cited work

Reference 19

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Observation 70c83c2c-388c-4224-ab92-11e058530a9f · outbound

This paper cites an unresolved cited work.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images Unresolved cited work

Reference 20

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Observation 6da76c6a-9375-4679-96ca-872172efda6b · outbound

This paper cites All the chosen statistics functions manage to summarize well the generated samples and produce predictions with a reasonably small distance to the ground truth value.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images All the chosen statistics functions manage to summarize well the generated samples and produce predictions with a reasonably small distance to the ground truth value

Reference 21

Resolution: verified exact; raw_fallback, observed 2026-08-10T14:10:43.991837Z

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Observation 74e8fef3-fab5-4bf9-9aa7-63994df231eb · outbound

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Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images Unresolved cited work

Reference 22

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Observation fc468677-2275-4d99-ba32-e0752de842c8 · outbound

This paper cites The evaluation result is presented in Table.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images The evaluation result is presented in Table

Reference 23

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Observation 863954c2-d9fd-41a8-8ab9-f8fc0701c475 · outbound

This paper cites Judging from the numerical results, we observe that larger pathology foundation models do not nec- essarily imply a better performance for Stem.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images Judging from the numerical results, we observe that larger pathology foundation models do not nec- essarily imply a better performance for Stem

Reference 24

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Observation d631e8ec-bfc5-444e-b3b7-2f15e936d05d · outbound

This paper cites However, the per- formance of Stem using combined UNI and CONCH still surpasses other approaches by a great margin.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images However, the per- formance of Stem using combined UNI and CONCH still surpasses other approaches by a great margin

Reference 25

Resolution: verified exact; raw_fallback, observed 2026-08-10T14:10:43.916130Z

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Observation 8295ab4d-5493-416c-8112-0ed7f63341ca · outbound

This paper cites We also present the gene variation curves of each method on this dataset in Fig.9.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images We also present the gene variation curves of each method on this dataset in Fig.9

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:44.237912Z

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Observation bb97012f-f249-4879-b83b-140e824a0b28 · outbound

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

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images Score-Based Generative Modeling through Stochastic Differential Equations

Reference 2015

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Observation 8edac2ca-d011-4935-963a-427acfda7795 · outbound

This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 2021

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Observation e7f299a7-e7a3-4b3d-9dca-15b73b50bbfe · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images Deep unsupervised learning using nonequilibrium thermodynamics

Reference 2022

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Observation c46f2497-a842-4d5c-83fb-62d8c0bc55fd · outbound

This paper cites HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis

Reference 2023

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Observation 665fe0e7-4d0a-44b0-958a-bd357e413086 · outbound

This paper cites Scaling self-supervised learning for histopathology with masked image modeling.

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images Scaling self-supervised learning for histopathology with masked image modeling

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:44.368678Z

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Pith citing papers

Observation eafaebdc-3435-46e0-ae89-b2651ea44185 · inbound

Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching cites this paper.

Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images

Reference 18

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Observation c87e7c81-ecde-466b-8e2d-f15bfd312fb8 · inbound

Latent Gene Diffusion for Spatial Transcriptomics Completion cites this paper.

Latent Gene Diffusion for Spatial Transcriptomics Completion Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images

Reference 31

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Observation 208923b8-9ea5-412c-88f6-1c8eb90e344a · inbound

The finite expression method for turbulent dynamics with high-order moment recovery cites this paper.

The finite expression method for turbulent dynamics with high-order moment recovery Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images

Reference 27

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Observation 3dd30d90-4c27-4e88-9e86-ff31cc1acd6e · inbound

DUET: Dual-Paradigm Adaptive Expert Triage with Single-cell Inductive Prior for Spatial Transcriptomics Prediction cites this paper.

DUET: Dual-Paradigm Adaptive Expert Triage with Single-cell Inductive Prior for Spatial Transcriptomics Prediction Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:25:03.898254Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2bd6ead6-4755-445c-8f61-e3b273cc3b7e · inbound

Gene Ontology-Guided Hierarchical Spatial Gene Expression Prediction from Histopathology Images cites this paper.

Gene Ontology-Guided Hierarchical Spatial Gene Expression Prediction from Histopathology Images Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T04:19:21.064757Z

Source-reported events for the cited work

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