Paper Citation Record · LEDGER

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg

As of 19 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2502.05320.

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

pith.paper-citation-record.v1
2502.05320 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:51:53.225575Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

33 of 33 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f31ab155-cd9f-46b4-b667-b27c856897d4 · outbound

This paper cites Building robust pathology image analyses with uncertainty quantification,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Building robust pathology image analyses with uncertainty quantification,

Reference 1

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Observation 546a94c6-7154-422f-9f53-90dae3a5fb6d · outbound

This paper cites Digital pathology: accurate technique for quantitative assessment of histological features in metabolic-associated fatty liver disease,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Digital pathology: accurate technique for quantitative assessment of histological features in metabolic-associated fatty liver disease,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.525666Z

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Observation 6aad9051-557f-47a4-9d51-b9645f2b8ddd · outbound

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

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg U-net: Convolutional networks for biomedical image segmen- tation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.517279Z

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Observation c29e0cb3-486c-4735-976b-b7369410cc4d · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Attention U-Net: Learning Where to Look for the Pancreas

Reference 4

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Source-reported events for the cited work

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Observation a6bcd6b9-350b-4eda-81f9-1502caf68b0c · outbound

This paper cites Unet 3+: A full-scale connected unet for medical image segmentation,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Unet 3+: A full-scale connected unet for medical image segmentation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.509089Z

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Observation baff6055-13b8-4de2-9fac-b4066d9ae064 · outbound

This paper cites Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.500716Z

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Observation 2a1778bc-92c7-47a6-819d-b6a629cd79b6 · outbound

This paper cites An integrated iterative annotation technique for easing neural network training in medical image analysis,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg An integrated iterative annotation technique for easing neural network training in medical image analysis,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.492279Z

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Observation 51224aa6-d774-4cd5-adbc-154e11ab721f · outbound

This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.483233Z

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Observation b17b9576-8b00-4deb-ae64-7ee35eda931c · outbound

This paper cites Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning for fine renal artery segmentation,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning for fine renal artery segmentation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.474879Z

Source-reported events for the cited work

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Observation 628ea6f9-3989-46e0-8bf4-1aadc4393cc3 · outbound

This paper cites Mo077 automatic segmentation of arteries, arterioles and glomeruli in native biopsies with thrombotic microangiopathy and other vascular diseases,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Mo077 automatic segmentation of arteries, arterioles and glomeruli in native biopsies with thrombotic microangiopathy and other vascular diseases,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.466441Z

Source-reported events for the cited work

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Observation ca234e7b-5640-464d-8fc8-6120fe1a1bdb · outbound

This paper cites An evaluation of U-Net in Renal Structure Segmentation.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg An evaluation of U-Net in Renal Structure Segmentation

Reference 11

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Observation 975c1ea5-90a2-44ac-9d57-bc4f0e266b2e · outbound

This paper cites Meta grayscale adaptive network for 3d integrated renal structures segmentation,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Meta grayscale adaptive network for 3d integrated renal structures segmentation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.457888Z

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Observation 1690dddc-7e10-446d-bdc0-e79368e898ae · outbound

This paper cites A hybrid approach to full-scale reconstruction of renal arterial network,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg A hybrid approach to full-scale reconstruction of renal arterial network,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.449372Z

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Observation cb280254-6492-46a0-b186-c0ea798e0ee2 · outbound

This paper cites Cor- tical thickness: an early morphological marker of atherosclerotic renal disease,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Cor- tical thickness: an early morphological marker of atherosclerotic renal disease,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.441033Z

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Observation 23a69b5d-e0f6-4efe-b77f-e2008da146a3 · outbound

This paper cites Acute kidney injury,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Acute kidney injury,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.432701Z

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Observation 93a14d00-d002-44a4-ac0e-2281a3f45d1e · outbound

This paper cites Mast cell quantification in normal peritoneum and during peritoneal dialysis treatment,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Mast cell quantification in normal peritoneum and during peritoneal dialysis treatment,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.424532Z

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Observation 2c5e9c1e-b34b-4216-a0dd-f16fb359a768 · outbound

This paper cites Omni-seg: A scale-aware dynamic network for renal pathological image segmentation,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Omni-seg: A scale-aware dynamic network for renal pathological image segmentation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.415913Z

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Observation 111b09d9-2a92-4a14-827f-78754351a8e1 · outbound

This paper cites Cpp-unet: Combined pyramid pooling modules in the u-net network for kidney, tumor and cyst segmentation,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Cpp-unet: Combined pyramid pooling modules in the u-net network for kidney, tumor and cyst segmentation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.407572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 04fffd70-32e2-452a-810c-a90298b04403 · outbound

This paper cites Rasnet: Renal automatic segmentation using an improved u-net with multi-scale perception and attention unit,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Rasnet: Renal automatic segmentation using an improved u-net with multi-scale perception and attention unit,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.399232Z

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Observation 133ab251-422b-4a81-848a-3c113a0dc2b5 · outbound

This paper cites Ma-unet: An improved version of unet based on multi-scale and attention mechanism for medical image segmentation,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Ma-unet: An improved version of unet based on multi-scale and attention mechanism for medical image segmentation,

Reference 20

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Observation cd6f45af-b422-4f78-8f6c-acf73cfc9f9c · outbound

This paper cites Karpinski score under digital investigation: a fully automated segmentation algorithm to identify vascular and stromal injury of donors’ kidneys,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Karpinski score under digital investigation: a fully automated segmentation algorithm to identify vascular and stromal injury of donors’ kidneys,

Reference 21

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Observation 87ef7b31-f56c-48aa-bf10-8dd062cffdea · outbound

This paper cites Ai applications in renal pathology,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Ai applications in renal pathology,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.374011Z

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Observation 55a4238a-f405-4e94-b430-20618c33d23d · outbound

This paper cites Artificial intelligence in renal pathology: current status and future,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Artificial intelligence in renal pathology: current status and future,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.365756Z

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Observation a04e4f9c-767c-4c58-960d-ba7922413ddc · outbound

This paper cites Evaluating tubulointerstitial compartments in renal biopsy speci- mens using a deep learning-based approach for classifying normal and abnormal tubules,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Evaluating tubulointerstitial compartments in renal biopsy speci- mens using a deep learning-based approach for classifying normal and abnormal tubules,

Reference 24

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Observation 4367c067-7397-466a-a740-be36677c89b4 · outbound

This paper cites Glomerulosclerosis iden- tification in whole slide images using semantic segmentation,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Glomerulosclerosis iden- tification in whole slide images using semantic segmentation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.348504Z

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Observation 906672f0-6df5-46df-9fc2-ec12e5293730 · outbound

This paper cites Automated assessment of glomerulosclerosis and tubular atrophy using deep learning,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Automated assessment of glomerulosclerosis and tubular atrophy using deep learning,

Reference 26

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Observation 7795f0c0-58f7-4355-a9d3-6379fa6f28e6 · outbound

This paper cites Deep learning–based segmentation and quantification in experimental kidney histopathology,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Deep learning–based segmentation and quantification in experimental kidney histopathology,

Reference 27

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Observation fb717523-8011-4749-8f8f-3bcdf523ba46 · outbound

This paper cites Smu-net: Style matching u-net for brain tumor segmentation with missing modalities,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Smu-net: Style matching u-net for brain tumor segmentation with missing modalities,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.321973Z

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Observation 8b298df4-f2fd-4d8f-8196-3335b17a7913 · outbound

This paper cites Ma-net: A multi-scale attention network for liver and tumor segmentation,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Ma-net: A multi-scale attention network for liver and tumor segmentation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.312273Z

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Observation 13e60082-1844-48a6-b344-d1da156518fd · outbound

This paper cites Sa-unet: Spatial attention u-net for retinal vessel segmentation,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Sa-unet: Spatial attention u-net for retinal vessel segmentation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.303389Z

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Observation 2bc5dd4e-3fe4-411a-9751-a705cb1cd412 · outbound

This paper cites Deep frequency re- calibration u-net for medical image segmentation,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Deep frequency re- calibration u-net for medical image segmentation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.294034Z

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Observation 8a875f3a-de05-4840-a87f-92a78a110f8b · outbound

This paper cites Omni-seg: A scale-aware dynamic network for renal pathological image segmentation,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Omni-seg: A scale-aware dynamic network for renal pathological image segmentation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.285220Z

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Observation 97db66d4-ea44-431a-9045-694bed9b7028 · outbound

This paper cites Unet++: A nested u-net architecture for medical image segmentation,.

Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg Unet++: A nested u-net architecture for medical image segmentation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T19:51:53.276099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.