Paper Citation Record · LEDGER

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization

As of 19 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2505.05073.

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

pith.paper-citation-record.v1
2505.05073 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:19:13.521945Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

28 of 28 outbound references displayed

verified exact0
verified fuzzy10
unresolved8
parse uncertain0
malformed identifier6
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9ee8402d-b250-4fa6-8d31-9292c6ffc1bb · outbound

This paper cites IEEE Trans- actions on Medical Imaging 32(12), 2169– 2178 (2013).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization IEEE Trans- actions on Medical Imaging 32(12), 2169– 2178 (2013)

Reference 1

Resolution: malformed identifier; no resolver link, observed 2026-08-15T23:19:13.391947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c52e8744-d002-428b-9459-6cc3dec1fd51 · outbound

This paper cites Cancer Research 75(15 Supplement), 285 (2015).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization Cancer Research 75(15 Supplement), 285 (2015)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:19:14.668586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cf52366c-4cb8-4d35-ba8a-05edd6fcc2ab · outbound

This paper cites A review of machine learning approaches, challenges and prospects for computational tumor pathology.

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization A review of machine learning approaches, challenges and prospects for computational tumor pathology

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T23:19:14.417679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 90f7eb9b-f13e-4b98-85d1-ba8b7edaf280 · outbound

This paper cites IEEE Transactions on Pattern Analysis & Machine Intelligence 13(06), 583–598 (1991).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization IEEE Transactions on Pattern Analysis & Machine Intelligence 13(06), 583–598 (1991)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:19:14.652812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 533dccd4-216b-4ab4-b935-384358f4cef9 · outbound

This paper cites IEEE Transactions on Circuits and Systems I: Regular Papers 53(11), 2405–2414 (2006).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization IEEE Transactions on Circuits and Systems I: Regular Papers 53(11), 2405–2414 (2006)

Reference 5

Resolution: metadata mismatch; raw_fallback, observed 2026-08-15T23:19:14.392835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fd984e0d-4d84-4470-81bc-dd8f31f246be · outbound

This paper cites Medical Image Anal- ysis 36, 135–146 (2017).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization Medical Image Anal- ysis 36, 135–146 (2017)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:19:14.636223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2c2c8836-4fb8-4601-a0e6-5ae0d1eb9a2b · outbound

This paper cites In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S.

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:19:14.620439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aa9bd5c9-826b-4349-92fb-161e4bed052c · outbound

This paper cites Medical Image Analysis 65, 101786 (2020).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization Medical Image Analysis 65, 101786 (2020)

Reference 8

Resolution: malformed identifier; no resolver link, observed 2026-08-15T23:19:13.427633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a53e9bdf-86af-46e5-8b5a-77771cdc69a4 · outbound

This paper cites IEEE Transactions on Medical Imag- ing 38(2), 448–459 (2019).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization IEEE Transactions on Medical Imag- ing 38(2), 448–459 (2019)

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T23:19:13.432035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 312db72d-a115-417c-9a72-0b548c48c120 · outbound

This paper cites Medical Image Analysis 58, 101563 (2019).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization Medical Image Analysis 58, 101563 (2019)

Reference 10

Resolution: malformed identifier; no resolver link, observed 2026-08-15T23:19:13.436581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5017fdfe-48a8-4ee8-b6f9-66a7b5b95214 · outbound

This paper cites PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification.

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T23:19:14.070542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6b470819-063e-4405-8639-e4a0a1f0b2c8 · outbound

This paper cites In: 2021 IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), pp.

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization In: 2021 IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), pp

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T23:19:13.445898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d93fdf66-07d5-40f0-8aec-8a83a6099c79 · outbound

This paper cites In: Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision (ICCV) Workshops, pp.

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization In: Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision (ICCV) Workshops, pp

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:19:14.604451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7c18d8ed-94ac-4cbd-b5b0-29f3b0bbfdff · outbound

This paper cites CoNIC: Colon Nuclei Identification and Counting Challenge 2022.

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization CoNIC: Colon Nuclei Identification and Counting Challenge 2022

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T23:19:13.455643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1135e3fe-0388-4897-9b2f-7056b3585278 · outbound

This paper cites Medical Image Analysis 92, 103047 (2024).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization Medical Image Analysis 92, 103047 (2024)

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T23:19:13.460982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb9b1e23-0684-4940-8e3a-0e2fe29671a3 · outbound

This paper cites Medical Image Analysis 35, 489–502 (2017).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization Medical Image Analysis 35, 489–502 (2017)

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T23:19:13.465527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 559bfa25-69d6-4d8e-966c-62a742bc7154 · outbound

This paper cites Medical Image Analysis 52, 199–211 (2019).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization Medical Image Analysis 52, 199–211 (2019)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T23:19:13.470187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71977296-597c-4358-b730-805fa679def0 · outbound

This paper cites Medical Image Analysis 80, 102485 (2022).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization Medical Image Analysis 80, 102485 (2022)

Reference 18

Resolution: malformed identifier; no resolver link, observed 2026-08-15T23:19:13.474557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 885397a7-c00a-4400-8ef7-d662c6537566 · outbound

This paper cites In: Reyes-Aldasoro, C.C., Janowczyk, A., Veta, M., Bankhead, P., Sirinukunwat- tana, K.

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization In: Reyes-Aldasoro, C.C., Janowczyk, A., Veta, M., Bankhead, P., Sirinukunwat- tana, K

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:19:14.587173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 09810191-dff9-473a-950a-5a6ed715e172 · outbound

This paper cites Multi-Task Learning in Histo-pathology for Widely Generalizable Model.

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization Multi-Task Learning in Histo-pathology for Widely Generalizable Model

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T23:19:13.834914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7de772bf-e382-43de-9943-eaa5242cccc9 · outbound

This paper cites New England Journal of Medicine 375(12), 1109–1112 (2016).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization New England Journal of Medicine 375(12), 1109–1112 (2016)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:19:14.569999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f235bda2-ba24-47ba-9326-2712622827de · outbound

This paper cites In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F.

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T23:19:13.493245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7f47d61-e329-421f-9ee6-0ce7cd2ad1f7 · outbound

This paper cites an unresolved cited work.

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:19:14.554703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d460d4ef-864d-436c-9119-885c20e1c372 · outbound

This paper cites IEEE Transactions on Medical Imaging 36(7), 1550–1560 (2017).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization IEEE Transactions on Medical Imaging 36(7), 1550–1560 (2017)

Reference 24

Resolution: malformed identifier; no resolver link, observed 2026-08-15T23:19:13.502761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9798c39d-8131-4ea1-babc-8614efe898fd · outbound

This paper cites Frontiers in bioengineering and biotech- nology, 53 (2019).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization Frontiers in bioengineering and biotech- nology, 53 (2019)

Reference 25

Resolution: malformed identifier; no resolver link, observed 2026-08-15T23:19:13.507635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99934c02-3388-4324-b0a1-465f4fd5d963 · outbound

This paper cites In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11, pp.

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11, pp

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:19:14.539481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f313ba16-76c9-4b85-9162-e10516d51048 · outbound

This paper cites In: Proceedings of the IEEE International Conference on Com- puter Vision, pp.

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization In: Proceedings of the IEEE International Conference on Com- puter Vision, pp

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:19:14.522708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b425df9a-1630-403b-823d-5435690ed9af · outbound

This paper cites In: Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019).

RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization In: Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:19:14.506805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.