Paper Citation Record · LEDGER

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation

As of 13 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2411.16515.

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

pith.paper-citation-record.v1
2411.16515 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:06:18.887708Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:21:51.021917Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T20:21:51.141278Z

Reference resolution

43 of 43 outbound references displayed

verified exact1
verified fuzzy24
unresolved18
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eab93229-f808-4d1f-9d8a-e449ade0ffd3 · outbound

This paper cites an unresolved cited work.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Unresolved cited work

Reference 1

Resolution: verified exact; doi, observed 2026-08-12T13:06:18.914649Z

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Observation 521f99b8-0333-40d3-b91d-68ab3e5a0789 · outbound

This paper cites Classification of melanocytic lesions in selected and whole-slide images via convolutional neural networks,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Classification of melanocytic lesions in selected and whole-slide images via convolutional neural networks,

Reference 2

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Observation 500757d3-5e3b-4d16-a5de-ccb7bc425821 · outbound

This paper cites Segmentation methods of h&e-stained histological images of lymphoma: a review,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Segmentation methods of h&e-stained histological images of lymphoma: a review,

Reference 3

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Observation aa07f7cc-2bba-430b-acb4-aa1bf88b6394 · outbound

This paper cites A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.306925Z

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Observation b58aeeab-d1d6-4e98-81b4-038a8fa17ede · outbound

This paper cites A deep learning approach for semantic segmenta- tion in histology tissue images,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation A deep learning approach for semantic segmenta- tion in histology tissue images,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.297505Z

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Observation 68868d83-7304-46f9-9642-22000529922c · outbound

This paper cites Deep learning in big image data: Histology image classification for breast cancer diagnosis,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Deep learning in big image data: Histology image classification for breast cancer diagnosis,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.288434Z

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Observation 8ddd12fb-cd3c-462a-a016-f2e5ca3471aa · outbound

This paper cites Using deep learning to enhance cancer diagnosis and classification,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Using deep learning to enhance cancer diagnosis and classification,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.279444Z

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Observation 659b4256-72b3-4475-a37b-72d0eb4b4c4a · outbound

This paper cites High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks

Reference 8

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Source-reported events for the cited work

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Observation 8b71862f-9cb7-4225-8c71-3e0f14d5eef3 · outbound

This paper cites Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.270102Z

Source-reported events for the cited work

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Observation 2f2d1220-6271-42f2-a51e-1f5eb7e2efd1 · outbound

This paper cites Harnessing artificial intelligence to infer novel spatial biomarkers for the diagnosis of eosinophilic esophagitis,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Harnessing artificial intelligence to infer novel spatial biomarkers for the diagnosis of eosinophilic esophagitis,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.259862Z

Source-reported events for the cited work

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Observation 214fc180-0bcc-47e7-a6b2-4f5be3b0a184 · outbound

This paper cites Machine learning approach for biopsy-based identification of eosinophilic esophagitis reveals importance of global features,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Machine learning approach for biopsy-based identification of eosinophilic esophagitis reveals importance of global features,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T13:06:18.789803Z

Source-reported events for the cited work

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Observation cc725d30-6340-42f1-96e8-34d4f7c4d549 · outbound

This paper cites A deep multi-label segmentation network for eosinophilic esophagitis whole slide biopsy diagnostics,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation A deep multi-label segmentation network for eosinophilic esophagitis whole slide biopsy diagnostics,

Reference 12

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Source-reported events for the cited work

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Observation 6936399c-b94f-410a-adcb-4fc1baa51b82 · outbound

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

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.239458Z

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Observation 27df7659-188b-4225-b997-88420f91e738 · outbound

This paper cites Deep learning in medical image analysis,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Deep learning in medical image analysis,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.230494Z

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Observation e8fbe872-a6a4-4c1d-8fe1-e4cd04ea8021 · outbound

This paper cites Harnessing artificial intelligence to infer novel spatial biomarkers for the diagnosis of eosinophilic esophagitis,.

Reference 15

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Source-reported events for the cited work

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Observation c01126ae-313b-47c5-8c50-34b6f2d663d5 · outbound

This paper cites Translational ai and deep learning in diagnostic pathology,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Translational ai and deep learning in diagnostic pathology,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T13:06:18.804000Z

Source-reported events for the cited work

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Observation d41e4b52-4d7b-4594-9351-42a2413cfd73 · outbound

This paper cites Artificial intelligence and digital pathology: challenges and opportunities,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Artificial intelligence and digital pathology: challenges and opportunities,

Reference 17

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Observation b74b4839-6ef7-4a48-b315-7674da49045a · outbound

This paper cites Pathologygan: Learning deep representations of cancer tissue,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Pathologygan: Learning deep representations of cancer tissue,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.211256Z

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Observation a7b12055-d5ad-4a4e-9879-998d9160d83f · 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,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Between generating noise and generating images: Noise in the correct frequency improves the quality of synthetic histopathology images for digital pathology,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.202395Z

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Observation d6766204-fbd7-4178-b246-e31db7fdec99 · outbound

This paper cites Generative adversarial networks for pre-training of medical image segmentation networks,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Generative adversarial networks for pre-training of medical image segmentation networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.193435Z

Source-reported events for the cited work

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Observation f6eeffb3-0112-4dc2-bed6-7854185a90b8 · outbound

This paper cites Synthetic Medical Images from Dual Generative Adversarial Networks.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Synthetic Medical Images from Dual Generative Adversarial Networks

Reference 21

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Observation aca746b4-cb10-4575-84e2-27935f0e5de9 · outbound

This paper cites High resolution histopathology image generation and segmentation through adversarial training,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation High resolution histopathology image generation and segmentation through adversarial training,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.184616Z

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Observation 90a697a7-1910-4d46-855d-472d77ff7e74 · outbound

This paper cites Generative adversarial networks,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Generative adversarial networks,

Reference 23

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Observation 4dfa4723-afbd-4b10-844c-3c3cff9185d8 · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 24

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Observation c8cc97db-e112-4784-9347-e40a87f78360 · outbound

This paper cites Depas: De-novo pathology semantic masks using a generative model,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Depas: De-novo pathology semantic masks using a generative model,

Reference 25

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Observation d93ed2b7-deaf-4f5a-a5b7-6908120dbf32 · outbound

This paper cites Review the cancer genome atlas (tcga): an immeasurable source of knowledge,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Review the cancer genome atlas (tcga): an immeasurable source of knowledge,

Reference 26

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Observation a0288ebd-0e9c-48f9-974c-1f8799dfb255 · outbound

This paper cites Pd-l1 expression in human cancers and its association with clinical outcomes,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Pd-l1 expression in human cancers and its association with clinical outcomes,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.153386Z

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Observation e4a861b2-b789-4475-ac5d-465c27d9823a · outbound

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

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 28

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Observation 039f4169-07d6-4bc1-98d2-89133549f842 · outbound

This paper cites Evaluating kolmogorov’s distribution,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Evaluating kolmogorov’s distribution,

Reference 29

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Observation 28afef3e-5a57-4184-b58a-ed4b9377409b · outbound

This paper cites an unresolved cited work.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Unresolved cited work

Reference 30

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Observation 9eb7f419-e527-4756-b9cf-5efa8c0d9f34 · outbound

This paper cites Unpaired image-to-image translation using cycle-consistent adversarial networks,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Unpaired image-to-image translation using cycle-consistent adversarial networks,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T13:06:18.850239Z

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Observation e50f9cb7-e122-4e0d-8618-4ad677cef8e3 · outbound

This paper cites Labelme: Image polygonal annotation with python.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Labelme: Image polygonal annotation with python

Reference 33

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Observation 71d4a321-78a3-487e-a77e-f1eb45a39c48 · outbound

This paper cites Medibang inc,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Medibang inc,

Reference 34

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Observation 143e9d7d-9742-4595-839c-cf2b5801d85b · outbound

This paper cites Samsung penup.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Samsung penup

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.102770Z

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Observation a3a94be3-3061-46bc-9aa7-7b9709981a52 · outbound

This paper cites Conditional Generative Adversarial Nets.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Conditional Generative Adversarial Nets

Reference 36

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Observation f7b65e14-f586-4949-870f-c30285e9a92c · outbound

This paper cites Medical image computing and computer-assisted intervention–miccai 2015,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Medical image computing and computer-assisted intervention–miccai 2015,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.093835Z

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Observation 1c0b2878-b364-4fe9-93d6-c59458dc59e0 · outbound

This paper cites Image-to-image translation with conditional adversarial networks,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Image-to-image translation with conditional adversarial networks,

Reference 38

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Observation 52343207-8618-406a-ace3-f9a345548e45 · outbound

This paper cites Deep residual learning for image recognition,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Deep residual learning for image recognition,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T13:06:18.873592Z

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This paper cites Pytorch: An imperative style, high-performance deep learning library,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Pytorch: An imperative style, high-performance deep learning library,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.074717Z

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Observation f904c01d-5a4a-4a47-a072-c6c5e6ff3160 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Adam: A Method for Stochastic Optimization

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T13:06:18.879155Z

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Observation 06b361ca-9194-4b17-99eb-7a6123ba3d25 · outbound

This paper cites High-resolution image synthesis and semantic manipulation with conditional gans,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation High-resolution image synthesis and semantic manipulation with conditional gans,

Reference 43

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Observation cc098720-735d-4c72-9f3d-3e5aa7b6e4d5 · outbound

This paper cites Synthesis of diagnostic quality cancer pathology images by generative adversarial networks,.

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation Synthesis of diagnostic quality cancer pathology images by generative adversarial networks,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.066137Z

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Observation 61f01976-f287-4978-a31a-23c4aac3ac90 · outbound

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

PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation A morphology focused diffusion probabilistic model for synthesis of histopathology images,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:06:19.057026Z

Source-reported events for the cited work

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Pith citing papers

Observation 46a623c5-4c24-4c52-83ad-5143babf71a1 · inbound

CSG: A Context-Semantic Guided Diffusion Approach in De Novo Musculoskeletal Ultrasound Image Generation cites this paper.

CSG: A Context-Semantic Guided Diffusion Approach in De Novo Musculoskeletal Ultrasound Image Generation PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:21:51.146893Z

Source-reported events for the cited work

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