Paper Citation Record · LEDGER

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation

As of 7 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2509.09267.

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

pith.paper-citation-record.v1
2509.09267 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:27:19.465479Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

18 of 18 outbound references displayed

verified exact0
verified fuzzy0
unresolved17
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 920cea32-ccdd-4fda-91f2-db4ce900822c · outbound

This paper cites This selective masking facilitates efficient model compression while preserving performance.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation This selective masking facilitates efficient model compression while preserving performance

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.465479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2c6dace-4a56-4782-93ba-552644066cc5 · outbound

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

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.393215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a78a213-8149-4f24-ae0d-9c80956b44b5 · outbound

This paper cites A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.403665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 610bcd06-45a7-451a-81cb-ac2976d58938 · outbound

This paper cites arXiv preprint arXiv:2106.14568.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation arXiv preprint arXiv:2106.14568

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.413796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 153bb01a-cc28-4904-bd98-e4002892c24f · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.423592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c10394b-e787-454c-9582-c097a94bef09 · outbound

This paper cites ShortGPT: Layers in Large Language Models are More Redundant Than You Expect.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.428144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8dca409-7f10-4497-aea3-9e9c2938ad84 · outbound

This paper cites LHU-Net: a Lean Hybrid U-Net for Cost-efficient, High-performance Volumetric Segmentation.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation LHU-Net: a Lean Hybrid U-Net for Cost-efficient, High-performance Volumetric Segmentation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.437568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f918305-b724-4969-9067-e18e79649304 · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation A Simple and Effective Pruning Approach for Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.442006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 819e0ca5-74bc-4946-9fcc-5f589034eca8 · outbound

This paper cites an unresolved cited work.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.451528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac013080-0bf6-423e-acd2-f77b099e9401 · outbound

This paper cites nnFormer: Interleaved Transformer for Volumetric Segmentation.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation nnFormer: Interleaved Transformer for Volumetric Segmentation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.456004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 642a762d-40d0-4a07-bdb2-7b0ab75d92a9 · outbound

This paper cites an unresolved cited work.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation Unresolved cited work

Reference 17

Resolution: malformed identifier; no resolver link, observed 2026-08-04T19:27:19.460435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b744893f-d543-46e9-aba8-09f8df1e1907 · outbound

This paper cites InMedical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, 234–241.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation InMedical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, 234–241

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.432589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 702f2e74-6cd3-440a-9981-df65f98e8351 · outbound

This paper cites Instance Normalization: The Missing Ingredient for Fast Stylization.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation Instance Normalization: The Missing Ingredient for Fast Stylization

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.446685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b388a3a0-add4-476c-89b5-6820c5ffb524 · outbound

This paper cites InMedical Image Computing and Com- puter Assisted Intervention–MICCAI 2019: 22nd Interna- tional Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III 22, 184–192.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation InMedical Image Computing and Com- puter Assisted Intervention–MICCAI 2019: 22nd Interna- tional Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III 22, 184–192

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.388360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2821133-9931-43bf-aa82-c438e5e8817c · outbound

This paper cites The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.382535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff9ef61a-1541-4934-ac12-827fb98ea876 · outbound

This paper cites Task-Specific Expert Pruning for Sparse Mixture-of-Experts.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation Task-Specific Expert Pruning for Sparse Mixture-of-Experts

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.398820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81a00c0b-0f9c-4052-b617-6e67841cf320 · outbound

This paper cites STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.408734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e503e305-eb19-4b77-b620-c66d5a4e5d81 · outbound

This paper cites Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models.

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:19.418335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.