Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 26 inbound Pith citation observations for arXiv:2304.06716.

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

pith.paper-citation-record.v1
2304.06716 v1

Coverage vector

measured 0 of 0 reference resolution

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measured 26 of 26 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 26 of 26 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T18:41:45.468086Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T16:49:58.527528Z

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Outbound references

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Pith citing papers

Observation 99675fd1-6574-437a-8cb4-07a5d6c1f574 · inbound

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

U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T12:36:49.910172Z

Source-reported events for the cited work

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Observation 23777f92-369e-46bc-a986-921844b1e557 · inbound

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images cites this paper.

QMaxViT-Unet+: A Query-Based MaxViT-Unet with Edge Enhancement for Scribble-Supervised Segmentation of Medical Images STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 15

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Observation 302425a8-a86b-4f13-ab83-27c4deb7c938 · inbound

Primus: Enforcing Attention Usage for 3D Medical Image Segmentation cites this paper.

Primus: Enforcing Attention Usage for 3D Medical Image Segmentation STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 30

Resolution: metadata mismatch; arxiv_id, observed 2026-05-23T01:25:16.530668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9c147ebc-94d6-4311-9322-78317e838adc · inbound

Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets cites this paper.

Good Enough? An Investigation on the Impact of Label Quality in Large-Scale Medical Datasets STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 1

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Observation b80166ba-42b2-4e6e-8356-15ca82e21fc2 · inbound

A Large Convolutional Neural Network for Clinical Target and Multi-organ Segmentation in Gynecologic Brachytherapy with Multi-stage Learning cites this paper.

A Large Convolutional Neural Network for Clinical Target and Multi-organ Segmentation in Gynecologic Brachytherapy with Multi-stage Learning STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:55:57.994806Z

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Observation d7d85a18-9bab-4024-8380-c72920867aea · inbound

BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis cites this paper.

BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T00:57:28.044812Z

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Observation 948f3d2e-0036-48da-aa36-c37f8ef1dbf2 · inbound

DpDNet: An Dual-Prompt-Driven Network for Universal PET-CT Segmentation cites this paper.

DpDNet: An Dual-Prompt-Driven Network for Universal PET-CT Segmentation STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 11

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Observation 49e66559-5d3b-45e6-96e4-30d57763165f · inbound

CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography cites this paper.

CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 52

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Observation cc53d2c9-8313-416f-abc8-ca303228598e · inbound

Rethink Domain Generalization in Heterogeneous Sequence MRI Segmentation cites this paper.

Rethink Domain Generalization in Heterogeneous Sequence MRI Segmentation STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T11:07:52.677578Z

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Observation da68f205-0616-4a33-8ce2-23eb68858145 · inbound

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting cites this paper.

MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T10:46:05.562057Z

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

Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation cites this paper.

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

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Observation c4a718e2-8bf0-4c06-8ad3-056180cffb8a · inbound

CardioBench: Do Echocardiography Foundation Models Generalize Beyond the Lab? cites this paper.

CardioBench: Do Echocardiography Foundation Models Generalize Beyond the Lab? STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T21:44:22.754084Z

Source-reported events for the cited work

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Observation aef9d04f-4f09-4e7a-9cb1-5edf368b4414 · inbound

Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI cites this paper.

Label-Efficient Cross-Modality Generalization for Liver Segmentation in Multi-Phase MRI STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T10:31:14.813822Z

Source-reported events for the cited work

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Observation a4b622d0-56cb-496b-814a-8c1b686bd243 · inbound

Benchmarking Deep Learning for Future Liver Remnant Segmentation in Colorectal Liver Metastasis cites this paper.

Benchmarking Deep Learning for Future Liver Remnant Segmentation in Colorectal Liver Metastasis STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T05:36:04.961741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 09abd286-c35b-427e-a367-281c42c7f1d4 · inbound

Camyla: Scaling Autonomous Research in Medical Image Segmentation cites this paper.

Camyla: Scaling Autonomous Research in Medical Image Segmentation STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 86

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T09:31:01.544586Z

Source-reported events for the cited work

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Observation fe9077b0-6b62-426b-931d-c0649d4ca5e3 · inbound

Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge cites this paper.

Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:15:58.069503Z

Source-reported events for the cited work

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Observation 836b7216-44c7-4791-92c2-d9b3af7ab44b · inbound

Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge cites this paper.

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-25T06:30:24.352326Z

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Observation da029659-b444-4853-9e40-af17bc0947a4 · inbound

TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT cites this paper.

TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 18

Resolution: malformed identifier; arxiv_id, observed 2026-05-20T18:58:53.947747Z

Source-reported events for the cited work

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Observation 1e10408c-a4b7-4506-95a0-c8f7b5524394 · inbound

GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT cites this paper.

GLeVE: Graph-Guided Lesion Grounding with Proposal Verification in 3D CT STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T06:44:42.080498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 463604c6-1b3c-4351-9a0d-9f14ecf52126 · inbound

Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes cites this paper.

Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T07:06:44.619040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9af86875-bb81-4e6b-a449-4efa49fcff9f · inbound

BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases cites this paper.

BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T16:49:58.529285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 072510ac-74ca-4804-bdf2-1a76701cdfd7 · inbound

LETT-NeXt: A Lightweight RECIST-Guided Model for 3D CT Lesion Segmentation cites this paper.

LETT-NeXt: A Lightweight RECIST-Guided Model for 3D CT Lesion Segmentation STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T06:54:20.431840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e2230bce-b9ab-4321-b36d-b50516ff05e3 · inbound

HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CT cites this paper.

HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CT STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 6

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HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CT cites this paper.

Reference 6

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Observation 4ef36c17-cf86-4dae-a1f7-2bb02810d77b · inbound

BATS: Resource-Efficient Volumetric Segmentation with Boundary-Aware Mixed-Resolution Tokens cites this paper.

BATS: Resource-Efficient Volumetric Segmentation with Boundary-Aware Mixed-Resolution Tokens STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-30T19:52:25.004673Z

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Observation 691eeee5-295f-47f6-9d19-1a830f1d0865 · inbound

Same Branches, Different Trees: A Bifurcation Connectedness Metric for Coronary Artery Segmentation and FFR-CT Decision Agreement cites this paper.

Same Branches, Different Trees: A Bifurcation Connectedness Metric for Coronary Artery Segmentation and FFR-CT Decision Agreement STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-31T11:15:22.091643Z

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Unavailable: canonical work link unavailable.