Paper Citation Record · LEDGER

Promptable cancer segmentation using minimal expert-curated data

As of 10 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2505.17915.

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

pith.paper-citation-record.v1
2505.17915 v1

Coverage vector

measured 34 of 34 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-07T14:43:34.293166Z

measured 34 of 34 standing notices

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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

34 of 34 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 74a2c2a9-ce42-499d-b571-50db919dba0b · outbound

This paper cites Early cancer detection using deep learning and medical imaging: A survey.

Promptable cancer segmentation using minimal expert-curated data Early cancer detection using deep learning and medical imaging: A survey

Reference 1

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Observation 2748ec15-0c7d-46e0-ba46-20e907f457d5 · outbound

This paper cites Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating con- firmatory study.

Promptable cancer segmentation using minimal expert-curated data Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating con- firmatory study

Reference 2

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Observation ddaeca51-b0cf-4733-80c9-9c0f5e61dba8 · outbound

This paper cites A systematic review on supervised and unsu- pervised machine learning algorithms for data science.

Promptable cancer segmentation using minimal expert-curated data A systematic review on supervised and unsu- pervised machine learning algorithms for data science

Reference 3

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Observation e6b1e28c-45bf-4f9b-bb25-f10b931ff104 · outbound

This paper cites Prostate-specific–antigen testing for early diagnosis of prostate cancer.

Promptable cancer segmentation using minimal expert-curated data Prostate-specific–antigen testing for early diagnosis of prostate cancer

Reference 4

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Observation 81e85945-a0a6-46c0-abfe-877cc2b8f7a8 · outbound

This paper cites Weakly-supervised deep learning model for prostate cancer diagnosis and gleason grading of histopathology im- ages.

Promptable cancer segmentation using minimal expert-curated data Weakly-supervised deep learning model for prostate cancer diagnosis and gleason grading of histopathology im- ages

Reference 5

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Observation 9d905a26-2763-41c6-bebf-817ad2b62860 · outbound

This paper cites The role of MRI/TRUS fusion biopsy in the diag- nosis of clinically significant prostate cancer.

Promptable cancer segmentation using minimal expert-curated data The role of MRI/TRUS fusion biopsy in the diag- nosis of clinically significant prostate cancer

Reference 6

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Observation f6f69dcc-e41a-4021-b8c5-fadf84ac004a · outbound

This paper cites Development and evaluation of intraoperative ul- trasound segmentation with negative image frames and multiple observer labels.

Promptable cancer segmentation using minimal expert-curated data Development and evaluation of intraoperative ul- trasound segmentation with negative image frames and multiple observer labels

Reference 7

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Observation 29aec783-2fea-4642-8958-e8f6890f95e5 · outbound

This paper cites Image registration: Fundamentals and recent advances based on deep learning.

Promptable cancer segmentation using minimal expert-curated data Image registration: Fundamentals and recent advances based on deep learning

Reference 8

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Observation 0ed4b5e4-956d-4739-8cfe-31699a825847 · outbound

This paper cites Progress in the cryoablation and cryoimmunotherapy for tumor.

Promptable cancer segmentation using minimal expert-curated data Progress in the cryoablation and cryoimmunotherapy for tumor

Reference 9

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Observation f6071ecf-d512-48b9-b8bd-785eda0b5ead · outbound

This paper cites Is segmentation uncertainty useful?.

Promptable cancer segmentation using minimal expert-curated data Is segmentation uncertainty useful?

Reference 10

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Observation 5424a534-8adb-4f67-a465-f8735ae6e182 · outbound

This paper cites Cryoablation: mechanism of action and devices.

Promptable cancer segmentation using minimal expert-curated data Cryoablation: mechanism of action and devices

Reference 11

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Observation 2618137f-4594-4b66-9c8c-77c3fbf49cc8 · outbound

This paper cites Segmentanything.

Promptable cancer segmentation using minimal expert-curated data Segmentanything

Reference 12

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Observation 254dcf50-0e3c-4660-bf59-86e4f215d757 · outbound

This paper cites Imagenet classi- fication with deep convolutional neural networks.

Promptable cancer segmentation using minimal expert-curated data Imagenet classi- fication with deep convolutional neural networks

Reference 13

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Observation 4b90f77e-0526-4a96-95ef-e0610648bbb6 · outbound

This paper cites Prism: A promptable and robust interactive segmenta- tion model with visual prompts.

Promptable cancer segmentation using minimal expert-curated data Prism: A promptable and robust interactive segmenta- tion model with visual prompts

Reference 14

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Observation 575e0e3a-f743-4184-a5a2-d055657eec62 · outbound

This paper cites Weaklysupervisedhistopathologyimagesegmentationwith self-attention.

Promptable cancer segmentation using minimal expert-curated data Weaklysupervisedhistopathologyimagesegmentationwith self-attention

Reference 15

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Observation 593a46ef-e467-4e61-ab6f-a48650c55be9 · outbound

This paper cites Segment Anything in Medical Images and Videos: Benchmark and Deployment.

Promptable cancer segmentation using minimal expert-curated data Segment Anything in Medical Images and Videos: Benchmark and Deployment

Reference 16

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Observation 13db4715-da58-4c0e-9483-78e7d1a887bf · outbound

This paper cites An Introduction to Convolutional Neural Networks.

Promptable cancer segmentation using minimal expert-curated data An Introduction to Convolutional Neural Networks

Reference 17

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Observation dc1482d6-6ca2-4f1d-9d32-9fb33c2bb114 · outbound

This paper cites Surgery, brachytherapy, and external-beam radiotherapy for early prostate cancer.

Promptable cancer segmentation using minimal expert-curated data Surgery, brachytherapy, and external-beam radiotherapy for early prostate cancer

Reference 18

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Observation 11d380d0-8098-4246-8c67-c5649564727c · outbound

This paper cites Weakly supervised localisation of prostate cancer using reinforcement learning for bi-parametric MR images.

Promptable cancer segmentation using minimal expert-curated data Weakly supervised localisation of prostate cancer using reinforcement learning for bi-parametric MR images

Reference 19

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Observation 12b0fd4c-72d2-4bee-9ee0-7a011fecfc85 · outbound

This paper cites Mixed supervision of histopathology improves prostate cancer classification from MRI.

Promptable cancer segmentation using minimal expert-curated data Mixed supervision of histopathology improves prostate cancer classification from MRI

Reference 20

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Observation 0dd5b50c-38ab-441c-a73a-17643c4553b9 · outbound

This paper cites Addressing AI algorithmic bias in health care.

Promptable cancer segmentation using minimal expert-curated data Addressing AI algorithmic bias in health care

Reference 21

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Observation b8ed14eb-3a16-4776-aca3-46fdd66e801d · outbound

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

Promptable cancer segmentation using minimal expert-curated data U-net: Convolu- tional networks for biomedical image segmentation

Reference 22

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Observation 7172949a-f105-4ae1-9fd4-5de29e7e809b · outbound

This paper cites Oncologic errors in diagnostic radiology: a 10-year analysis based on medical malpractice claims.

Promptable cancer segmentation using minimal expert-curated data Oncologic errors in diagnostic radiology: a 10-year analysis based on medical malpractice claims

Reference 23

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Observation d441869b-d438-43d4-bb4a-89542092b690 · outbound

This paper cites Competing for pixels: a self-play algorithm for weakly-supervised semantic segmentation.

Promptable cancer segmentation using minimal expert-curated data Competing for pixels: a self-play algorithm for weakly-supervised semantic segmentation

Reference 24

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Observation 9f05ae12-3eee-464b-8701-7b0c06a3a6e3 · outbound

This paper cites Image quality assessment by overlapping task- specific and task-agnostic measures: application to prostate multiparamet- ric MR images for cancer segmentation.

Promptable cancer segmentation using minimal expert-curated data Image quality assessment by overlapping task- specific and task-agnostic measures: application to prostate multiparamet- ric MR images for cancer segmentation

Reference 25

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Observation 5ece0390-671a-45d8-aa01-651f6c89dc26 · outbound

This paper cites The PI-CAI challenge: public training and develop- ment dataset.

Promptable cancer segmentation using minimal expert-curated data The PI-CAI challenge: public training and develop- ment dataset

Reference 26

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Observation d566be60-fdf7-41fc-a872-fc1464b113fc · outbound

This paper cites Cryoablation Treatment Guide.

Promptable cancer segmentation using minimal expert-curated data Cryoablation Treatment Guide

Reference 27

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Observation 4b279f4f-7ef6-4bb6-8c27-2a6650448848 · outbound

This paper cites Prostate cancer review: genetics, diagnosis, treatmentoptions, andalternativeapproaches.

Promptable cancer segmentation using minimal expert-curated data Prostate cancer review: genetics, diagnosis, treatmentoptions, andalternativeapproaches

Reference 28

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Observation 1f4af6c4-1afd-4ebd-9458-9d7028bd2776 · outbound

This paper cites Grad-CAM: visual explanations from deep networks via gradient-based localization.

Promptable cancer segmentation using minimal expert-curated data Grad-CAM: visual explanations from deep networks via gradient-based localization

Reference 29

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Observation 7ec2f5cb-2b08-4773-9b3c-0d19187b68f2 · outbound

This paper cites ProMISe: Promptable Medical Image Segmentation using SAM.

Promptable cancer segmentation using minimal expert-curated data ProMISe: Promptable Medical Image Segmentation using SAM

Reference 30

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Observation ddbbe0d5-da3f-448a-974f-dd9f83954969 · outbound

This paper cites Scribbleprompt: fast and flexible interactive segmen- tation for any biomedical image.

Promptable cancer segmentation using minimal expert-curated data Scribbleprompt: fast and flexible interactive segmen- tation for any biomedical image

Reference 31

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Observation cf3435b3-b6e3-4ee4-b9ed-86df057a10dd · outbound

This paper cites The impact of using voxel-level segmentation metrics on evaluating multifocal prostate cancer localisation.

Promptable cancer segmentation using minimal expert-curated data The impact of using voxel-level segmentation metrics on evaluating multifocal prostate cancer localisation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:43:34.864507Z

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Observation ed74de93-7a64-4144-9640-fd411663e97d · outbound

This paper cites T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging.

Promptable cancer segmentation using minimal expert-curated data T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:43:34.439663Z

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Observation 2e945cfd-a5b4-41ba-a1ca-a4c667fa0ce6 · outbound

This paper cites Grad-CAM-based explain- able artificial intelligence related to medical text processing.

Promptable cancer segmentation using minimal expert-curated data Grad-CAM-based explain- able artificial intelligence related to medical text processing

Reference 34

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Source-reported events for the cited work

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Pith citing papers

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