Paper Citation Record · LEDGER

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation

As of 11 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2607.06972.

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

pith.paper-citation-record.v1
2607.06972 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-09T00:55:54.931959Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

27 of 27 outbound references displayed

verified exact6
verified fuzzy20
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5d105cc4-d5b6-40e3-b3e2-38e5d10bd466 · outbound

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

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.550968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cedf071e-5ffa-4ad3-917c-c71b53a0778a · outbound

This paper cites nnu-net: A self-configuring method for deep learning-based biomedical image segmentation,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation nnu-net: A self-configuring method for deep learning-based biomedical image segmentation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.544819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1cca4d63-bfea-4f59-9b70-27f51310768b · outbound

This paper cites Segment anything,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Segment anything,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.565175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e683b7f9-fe1f-48af-a385-2290c803f0c9 · outbound

This paper cites Segment anything in medical images.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Segment anything in medical images

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.573754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8671b169-93d0-47a3-b0b3-e1117fbbc609 · outbound

This paper cites Customized Segment Anything Model for Medical Image Segmentation.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Customized Segment Anything Model for Medical Image Segmentation

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-07-09T01:05:49.524191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 63926675-ebab-43ed-9f9d-675301c4fc5e · outbound

This paper cites Medical sam adapter: Adapting segment anything model for medical image segmentation.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Medical sam adapter: Adapting segment anything model for medical image segmentation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.548971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 934413b8-9ba3-4786-808e-76c3e78448c5 · outbound

This paper cites Unleashing the potential of sam for medical adaptation via hierarchical decoding,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Unleashing the potential of sam for medical adaptation via hierarchical decoding,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.556351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5deefb2c-7aa9-4d89-b94c-be6c4f330a3b · outbound

This paper cites How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-07-09T01:05:49.514921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c2c5ec4b-4f03-4b22-b996-5cdeb869dfb1 · outbound

This paper cites PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-09T01:05:49.526979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 90e08965-eb75-46e0-a698-3290d09e4348 · outbound

This paper cites Surgicalsam: Efficient class promptable surgical instru- ment segmentation,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Surgicalsam: Efficient class promptable surgical instru- ment segmentation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.558123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 25e9f2dc-0653-4493-b65b-4ce07a228833 · outbound

This paper cites Prompting segment anything model with domain-adaptive prototype for generaliz- able medical image segmentation,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Prompting segment anything model with domain-adaptive prototype for generaliz- able medical image segmentation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.575474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2d97ea97-3d0f-437d-bcc8-583648caabc7 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-09T01:05:49.511543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 566d64f3-d1da-4089-9f45-a6c6ce8a8682 · outbound

This paper cites Unetr: Transformers for 3d medical image segmentation.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Unetr: Transformers for 3d medical image segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.540721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b666eafa-1e1e-444f-ae85-4163aaf1a837 · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmentation,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Swin-unet: Unet-like pure transformer for medical image segmentation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.552745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ba315f59-3982-4574-a359-cadffad219d8 · outbound

This paper cites SAM-Med2D.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation SAM-Med2D

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-07-09T01:05:49.521018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9885b6f1-c59a-4a44-8a1c-9b8f72539144 · outbound

This paper cites A probabilistic u-net for segmentation of ambiguous images,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation A probabilistic u-net for segmentation of ambiguous images,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.546829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 71cfcafe-fb86-4129-840c-17494300184f · outbound

This paper cites Phiseg: Capturing uncertainty in medical image segmentation,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Phiseg: Capturing uncertainty in medical image segmentation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.542718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6a86daa3-c5cf-4cee-993e-01a2efa984ae · outbound

This paper cites Effective semi-supervised medical image segmentation with probabilistic representations and pro- totype learning,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Effective semi-supervised medical image segmentation with probabilistic representations and pro- totype learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.561672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d810fb82-3722-4895-af21-6702aa7f215e · outbound

This paper cites A hierarchical probabilistic u-net for modeling multi-scale ambiguities,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation A hierarchical probabilistic u-net for modeling multi-scale ambiguities,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.568627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c0ae89db-2dd7-47bb-b5f2-6f05447c434a · outbound

This paper cites Miccai 2015 multi-atlas labeling beyond the cranial vault workshop and challenge,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Miccai 2015 multi-atlas labeling beyond the cranial vault workshop and challenge,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.570378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bb322496-aecd-411c-bd11-cec67c01dde4 · outbound

This paper cites Global registration of left atrium images for multi-atlas based segmentation,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Global registration of left atrium images for multi-atlas based segmentation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.554509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fc44808b-8ea7-4087-9dcb-8b003b07123f · outbound

This paper cites Evaluation of prostate segmentation algorithms for mri: The promise12 challenge,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Evaluation of prostate segmentation algorithms for mri: The promise12 challenge,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.559840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b6c25190-2ff1-482f-b290-af420b1befd0 · outbound

This paper cites Pg-sam: A fine-grained prior-guided sam framework for prompt-free medical image segmentation,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Pg-sam: A fine-grained prior-guided sam framework for prompt-free medical image segmentation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.563481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3bf539b4-2f68-4284-a778-2a43641c4fa7 · outbound

This paper cites TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation TransDeepLab: Convolution-Free Transformer-based DeepLab v3+ for Medical Image Segmentation

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-07-09T01:05:49.518136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1dd76dc8-7d5b-4379-bc7d-d5e228de94df · outbound

This paper cites DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-07-09T01:05:49.508503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c712ced0-a7ca-4b34-8ce3-604adec8aed0 · outbound

This paper cites Multi-scale hierarchical vision transformer with cascaded attention decoding for medical image seg- mentation,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Multi-scale hierarchical vision transformer with cascaded attention decoding for medical image seg- mentation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.566901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ddf1509b-d781-43ed-8114-fe7b2ae7f17d · outbound

This paper cites Sam-adapter: Adapting segment anything in underperformed scenes,.

HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation Sam-adapter: Adapting segment anything in underperformed scenes,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T01:05:49.572106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.