Paper Citation Record · LEDGER

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation

As of 20 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2607.07019.

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

pith.paper-citation-record.v1
2607.07019 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-09T21:40:31.628704Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

22 of 22 outbound references displayed

verified exact4
verified fuzzy18
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d55b8081-dd5d-4356-9a29-b0dbff055690 · outbound

This paper cites 3D U-Net: Learning dense volumetric segmentation from sparse annotation,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation 3D U-Net: Learning dense volumetric segmentation from sparse annotation,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.812607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5e24804b-d687-4e87-beba-bac226874a86 · outbound

This paper cites nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.828324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 82931825-e591-47c8-a3ea-53bb9d83f9ae · outbound

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

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation PG-SAM: A fine-grained prior-guided SAM framework for prompt-free medical image segmentation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.808641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c092fd43-1cb1-41c9-8278-cffa48abca5f · outbound

This paper cites TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.826498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b76cb0d6-6fc2-4bf3-80f2-c30fbb459f37 · outbound

This paper cites Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmenta- tion,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmenta- tion,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.806609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 132f2d14-3d84-489e-a949-5eea62828e9e · outbound

This paper cites Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.824431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 70103fcc-5c6a-41ec-8d77-5b567297aa41 · outbound

This paper cites Semi-supervised semantic segmentation with cross pseudo supervision,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation Semi-supervised semantic segmentation with cross pseudo supervision,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.830968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2d861d3c-7a1c-4a7a-ac95-02dc90b90105 · outbound

This paper cites Exploring smoothness and class-separation for semi-supervised medical image segmentation,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation Exploring smoothness and class-separation for semi-supervised medical image segmentation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.802641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a4b1d932-001d-42c4-b1eb-d8d14cda27a7 · outbound

This paper cites Bidirectional copy-paste for semi-supervised medical image segmentation,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation Bidirectional copy-paste for semi-supervised medical image segmentation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.822602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c71ad7a4-b2ee-49c5-b832-1252658e10de · outbound

This paper cites MagicNet: Semi-supervised multi-organ segmentation via magic-cube partition and recovery,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation MagicNet: Semi-supervised multi-organ segmentation via magic-cube partition and recovery,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.836601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fc3edbde-9154-4a6b-b211-dac50233b8d8 · outbound

This paper cites Adaptive bidirectional displace- ment for semi-supervised medical image segmentation,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation Adaptive bidirectional displace- ment for semi-supervised medical image segmentation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.834716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 11026327-1b10-41e5-87ec-d144c811f96c · outbound

This paper cites Dual-debiased heterogeneous co-training frame- work for class-imbalanced semi-supervised medical image segmenta- tion,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation Dual-debiased heterogeneous co-training frame- work for class-imbalanced semi-supervised medical image segmenta- tion,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.800432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2976e31d-090f-4666-be19-3e758e81b263 · outbound

This paper cites A semantic knowledge complementarity based decoupling framework for semi-supervised class-imbalanced medical image segmentation,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation A semantic knowledge complementarity based decoupling framework for semi-supervised class-imbalanced medical image segmentation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.818946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8a4ad0cf-ecf3-4f25-a60a-db8f3ae5f65b · outbound

This paper cites Gradient-aware for class-imbalanced semi-supervised medical image segmentation,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation Gradient-aware for class-imbalanced semi-supervised medical image segmentation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.832793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b0d6a798-3fe9-40f5-b740-6fd3befa1b70 · outbound

This paper cites Divide, conquer, and aggregate: Asymmetric experts for class- imbalanced semi-supervised medical image segmentation,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation Divide, conquer, and aggregate: Asymmetric experts for class- imbalanced semi-supervised medical image segmentation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.804526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 95a10be6-43c8-4226-87d1-3cb651b4cafe · outbound

This paper cites All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentation

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:46:34.617475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 815c9cd4-7db4-4b19-aae7-7f89aa23b1ca · outbound

This paper cites Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:46:34.611080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1b78f167-74da-4f39-a01f-a6257ceb949b · outbound

This paper cites Mixed Prototype Consistency Learning for Semi-supervised Medical Image Segmentation.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation Mixed Prototype Consistency Learning for Semi-supervised Medical Image Segmentation

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:46:34.614171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3fe031ce-3201-4569-aff4-3e29e04411f5 · outbound

This paper cites Prototype Contrastive Consistency Learning for Semi-Supervised Medical Image Segmentation.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation Prototype Contrastive Consistency Learning for Semi-Supervised Medical Image Segmentation

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:46:34.607780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f23b558e-f26f-4ace-a40c-c273f13e36e5 · outbound

This paper cites Inherent consistent learning for accurate semi-supervised medical image segmentation,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation Inherent consistent learning for accurate semi-supervised medical image segmentation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.810708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f8b3421f-2d57-46cd-88da-f3322dc5f285 · outbound

This paper cites Multi-atlas labeling beyond the cranial vault,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation Multi-atlas labeling beyond the cranial vault,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.814766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 30897536-16d3-4390-852b-13f886cced5b · outbound

This paper cites AMOS: A large-scale abdominal multi- organ benchmark for versatile medical image segmentation,.

SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation AMOS: A large-scale abdominal multi- organ benchmark for versatile medical image segmentation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T21:46:34.817153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.