Paper Citation Record · LEDGER

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging

As of 23 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2501.09185.

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

pith.paper-citation-record.v1
2501.09185 v2

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:12:42.334934Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

25 of 25 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f7780034-956f-4034-9b28-85be50d2f1d8 · outbound

This paper cites Cancer statistics,.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Cancer statistics,

Reference 1

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Source-reported events for the cited work

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Observation 1d58abe5-8219-4062-8b25-c24a41139d6b · outbound

This paper cites Prostate cancer facts.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Prostate cancer facts

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.826409Z

Source-reported events for the cited work

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Observation 2d5c9425-e937-44ee-b408-edc6064e65c4 · outbound

This paper cites What is the psa test? https://www.cancer.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging What is the psa test? https://www.cancer

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.811132Z

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Observation c7f5ef2e-118a-4bcd-9466-1925be02bd65 · outbound

This paper cites Overdiagnosis and overtreatment of prostate cancer.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Overdiagnosis and overtreatment of prostate cancer

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.785739Z

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Observation 89c22897-e33f-47a0-81a9-6fe7db794657 · outbound

This paper cites an unresolved cited work.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Unresolved cited work

Reference 5

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Source-reported events for the cited work

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Observation 203eff66-fe3e-4852-a976-b214f4c2979b · outbound

This paper cites Fully automated deep learning model to detect clinically significant prostate cancer at mri.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Fully automated deep learning model to detect clinically significant prostate cancer at mri

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.755409Z

Source-reported events for the cited work

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Observation 10f2f7a1-67d7-4fbe-84d8-8c5f09c2be56 · outbound

This paper cites Multiparametric mri for prostate cancer diagnosis: current status and future directions.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Multiparametric mri for prostate cancer diagnosis: current status and future directions

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.770841Z

Source-reported events for the cited work

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Observation 2d2828ec-8773-4ada-88b2-d14b6706812d · outbound

This paper cites Deep learning for fully automatic detection, segmentation, and gleason grade estimation of prostate cancer in multiparametric magnetic reso- nance images.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Deep learning for fully automatic detection, segmentation, and gleason grade estimation of prostate cancer in multiparametric magnetic reso- nance images

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.723814Z

Source-reported events for the cited work

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Observation 576ce01a-b66c-42a1-bb38-2b2f9bba3017 · outbound

This paper cites Explainable ai for cnn-based prostate tumor segmentation in multi- parametric mri correlated to whole mount histopathology.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Explainable ai for cnn-based prostate tumor segmentation in multi- parametric mri correlated to whole mount histopathology

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.739562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5e439484-b48d-41de-b846-03a3607280ed · outbound

This paper cites Enhancing clinical support for breast cancer with deep learning models using synthetic correlated diffusion imaging.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Enhancing clinical support for breast cancer with deep learning models using synthetic correlated diffusion imaging

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.693292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 22f59a05-6f0e-4cfe-97a4-b89e41d6b309 · outbound

This paper cites Syn- thetic correlated diffusion imaging hyperintensity delineates clinically significant prostate cancer.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Syn- thetic correlated diffusion imaging hyperintensity delineates clinically significant prostate cancer

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.708353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 07c7050c-3197-4322-8602-17d034877691 · outbound

This paper cites Cancer-net pca-data: An open-source benchmark dataset for prostate cancer clinical decision support using synthetic correlated diffusion imaging data.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Cancer-net pca-data: An open-source benchmark dataset for prostate cancer clinical decision support using synthetic correlated diffusion imaging data

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.661083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fba3a9d2-8bae-4a19-8d78-7b7f64139804 · outbound

This paper cites A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.677945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e74d49ab-5525-43c3-868a-1f8dc3c2305a · outbound

This paper cites Prostatex challenge data [data set], 2017.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Prostatex challenge data [data set], 2017

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.629758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f42be4c8-d7bb-4b84-bf3b-0ebfd9ad3c2c · outbound

This paper cites Computer-aided detection of prostate cancer in mri.IEEE Transactions on Medical Imaging , 33(5):1083–1092, 2014.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Computer-aided detection of prostate cancer in mri.IEEE Transactions on Medical Imaging , 33(5):1083–1092, 2014

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.645621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fc34ae73-0781-45ef-b853-3b5b0bed525f · outbound

This paper cites The cancer imaging archive (tcia): Maintain- ing and operating a public information repository.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging The cancer imaging archive (tcia): Maintain- ing and operating a public information repository

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.598331Z

Source-reported events for the cited work

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Observation e8facc4b-cb83-4818-8166-b959295e179c · outbound

This paper cites Quality control and whole-gland, zonal and lesion anno- tations for the prostatex challenge public dataset.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Quality control and whole-gland, zonal and lesion anno- tations for the prostatex challenge public dataset

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.613982Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 17682849-5ff8-4f74-8a57-21f5db27dc3a · outbound

This paper cites 3d mri brain tumor segmentation using autoencoder regular- ization.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging 3d mri brain tumor segmentation using autoencoder regular- ization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.571362Z

Source-reported events for the cited work

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Observation d993e160-09ce-48f8-ad4f-f645926166a9 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging U-net: Convolutional networks for biomedical image segmentation

Reference 19

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Source-reported events for the cited work

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Observation fd164897-1cf8-4ae0-83f9-c5a6a51d3b1e · outbound

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

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Attention U-Net: Learning Where to Look for the Pancreas

Reference 20

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Source-reported events for the cited work

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Observation 3d5421df-397f-4c4a-b330-5c937c800194 · outbound

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

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.555329Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9200d8af-c061-4a8a-8f07-5eef8e17aea0 · outbound

This paper cites Monai: Medical open network for ai.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Monai: Medical open network for ai

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.539450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0a72050f-c4bf-4a48-82ff-40089f8de92c · outbound

This paper cites LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation

Reference 23

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Source-reported events for the cited work

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Observation 4a63d331-bb4e-4999-81ca-b21f58bd3671 · outbound

This paper cites Thop: Pytorch-opcounter.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Thop: Pytorch-opcounter

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:12:42.522484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 18d82697-d243-400f-b09b-4c183ec6bbe2 · outbound

This paper cites an unresolved cited work.

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:12:42.843396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.