Paper Citation Record · LEDGER

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification

As of 7 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2604.07141.

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

pith.paper-citation-record.v1
2604.07141 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T17:56:12.977637Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

36 of 36 outbound references displayed

verified exact2
verified fuzzy34
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 923d8107-ee53-48b3-b2d6-35c543666f11 · outbound

This paper cites Epidemiological trends of urolithiasis at the global, regional, and national levels: a population-based study.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Epidemiological trends of urolithiasis at the global, regional, and national levels: a population-based study

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.786864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3be08bc7-93c0-4524-9c11-f0c866ee1224 · outbound

This paper cites Epidemiological research progress on urological stones and stone composition.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Epidemiological research progress on urological stones and stone composition

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.836642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d9e4ba91-2d34-4845-affa-daf9aa1988f1 · outbound

This paper cites Prevalence of kidney stones in mainland china: A systematic review.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Prevalence of kidney stones in mainland china: A systematic review

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.853419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 65691557-f29c-482e-b079-38194062d129 · outbound

This paper cites Stone composition pattern of kidney stone.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Stone composition pattern of kidney stone

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.871966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation aebdb99c-ada5-4f4a-a0ed-77977bead3ae · outbound

This paper cites Re- search advances of ct and ai technology in predicting the composition of urinary calculi.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Re- search advances of ct and ai technology in predicting the composition of urinary calculi

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.801827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bf54498e-0a28-4195-bc8e-da225d07e3bd · outbound

This paper cites Eau guidelines on diagnosis and conservative management of urolithiasis.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Eau guidelines on diagnosis and conservative management of urolithiasis

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.845429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f9c5c2db-b9fb-461b-9eae-780b7496134d · outbound

This paper cites Medical management of kidney stones: Aua guide- line.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Medical management of kidney stones: Aua guide- line

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.833976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation aadcad0d-6d3d-4643-944f-961e838a3308 · outbound

This paper cites Kidney stone prediction based on urine analysis using ensemble learn- ing.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Kidney stone prediction based on urine analysis using ensemble learn- ing

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.885911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c9c174ac-afc9-4ff0-a283-b7932523e87a · outbound

This paper cites What is the state of the stone analysis techniques in urolithiasis?.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification What is the state of the stone analysis techniques in urolithiasis?

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.819863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2c9d5af6-f1e1-4ad2-a16c-8f00bf3588af · outbound

This paper cites Deep learning for medical image processing: Overview, challenges and the future.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Deep learning for medical image processing: Overview, challenges and the future

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.822886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7a3a3e72-366b-4c3e-b077-4badb49503e7 · outbound

This paper cites New and evolving concepts in the imaging and management of urolithi- asis: urologists’ perspective.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification New and evolving concepts in the imaging and management of urolithi- asis: urologists’ perspective

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.798810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e7427a87-3fed-49a5-a2a4-c38ea6c43fea · outbound

This paper cites Vision transformers, ensemble model, and transfer learning leveraging explainable ai for brain tumor detection and classification.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Vision transformers, ensemble model, and transfer learning leveraging explainable ai for brain tumor detection and classification

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.794571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 11cafd07-ee8a-4a4e-aa41-86d3ce8c4403 · outbound

This paper cites Hybrid neural network framework for multiclass classification of kidney stones from ct scans.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Hybrid neural network framework for multiclass classification of kidney stones from ct scans

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.828459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e9f72335-c7dc-4f8e-a055-d2e28c1c0992 · outbound

This paper cites Clinical- inspired framework for automatic kidney stone recognition and analysis on transverse ct images.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Clinical- inspired framework for automatic kidney stone recognition and analysis on transverse ct images

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.878179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fce43984-52be-444f-9a7b-fc5b69fd63fa · outbound

This paper cites Advances on artificial intelligence in the diagnosis and treatment of urinary calculi.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Advances on artificial intelligence in the diagnosis and treatment of urinary calculi

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.831120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2996d054-de8d-4baa-accd-dd3547a0de23 · outbound

This paper cites Deep residual learning for image recognition.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Deep residual learning for image recognition

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.882398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3653311a-ee64-4b55-99db-456b259f9761 · outbound

This paper cites Hines, J.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Hines, J

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.850743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 38e5cf91-8aa5-4dcf-bdf4-26c57dfb1442 · outbound

This paper cites Deep learning in medical image analysis.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Deep learning in medical image analysis

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.804827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 36bcbd5b-0294-4519-811e-14fd333ce2a6 · outbound

This paper cites Stonenet: An efficient lightweight model based on depthwise separable convolutions for kidney stone detection from ct images.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Stonenet: An efficient lightweight model based on depthwise separable convolutions for kidney stone detection from ct images

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.813908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation dcef2bba-e8a1-459c-9fba-9c2b3dd80bb0 · outbound

This paper cites A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast ct scans.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast ct scans

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.842420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 40912fb5-5e95-4294-9696-698a4d4e0f58 · outbound

This paper cites Application of kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal ct images.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Application of kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal ct images

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.848123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f393c9e9-ad86-416e-ade4-5645275ff830 · outbound

This paper cites Resganet: Residual group attention network for medical image classification and segmentation.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Resganet: Residual group attention network for medical image classification and segmentation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.825639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4cffd29f-bf0b-41e7-bd12-03b9b4f4c276 · outbound

This paper cites HyMNet: a Multimodal Deep Learning System for Hypertension Classification using Fundus Photographs and Cardiometabolic Risk Factors.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification HyMNet: a Multimodal Deep Learning System for Hypertension Classification using Fundus Photographs and Cardiometabolic Risk Factors

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:51:00.375765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a6f804a4-c246-4f48-852b-1277c9e18356 · outbound

This paper cites Ich-prnet: a cross-modal intracerebral haemorrhage prog- nostic prediction method using joint-attention interaction mechanism.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Ich-prnet: a cross-modal intracerebral haemorrhage prog- nostic prediction method using joint-attention interaction mechanism

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.816940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 999a04d4-3917-4d5f-ac5b-4f57aab89804 · outbound

This paper cites Ich-scnet: Intracerebral hemorrhage segmentation and prog- nosis classification network using clip-guided sam mechanism.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Ich-scnet: Intracerebral hemorrhage segmentation and prog- nosis classification network using clip-guided sam mechanism

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.783293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b8b35e4f-1393-4917-abab-d65ceaaa319c · outbound

This paper cites Attention is all you need.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Attention is all you need

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.862648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 30b6270f-1ffd-4131-ae76-4b7bee31211e · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-11T05:51:00.370182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a4be3ae9-26ee-4001-9d01-6c6d2998efda · outbound

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

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Unetr: Transformers for 3d medical image segmentation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.875008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fe0cb031-39e8-42d3-af68-ff53ea6ee9a0 · outbound

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

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification U-net: Convolutional net- works for biomedical image segmentation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.839576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ee6586a1-578f-4fc0-9038-e3526a90518a · outbound

This paper cites Hybrid masked image modeling for 3d medical image segmentation.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Hybrid masked image modeling for 3d medical image segmentation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.865858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0f2a4e34-85a5-4e26-8174-f7d6aa8fac3a · outbound

This paper cites Segprompt: Using segmentation map as a better prompt to finetune deep models for kidney stone classification.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Segprompt: Using segmentation map as a better prompt to finetune deep models for kidney stone classification

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.807681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1125c8a8-3b93-4fdb-b2ad-02baabd935d2 · outbound

This paper cites Tmss: an end- to-end transformer-based multimodal network for segmentation and survival prediction.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Tmss: an end- to-end transformer-based multimodal network for segmentation and survival prediction

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.856596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 784513a2-d498-40e1-b7ad-8725996c5e81 · outbound

This paper cites Ehr-hgcn: An enhanced hybrid approach for text classification using heterogeneous graph convolutional networks in electronic health records.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Ehr-hgcn: An enhanced hybrid approach for text classification using heterogeneous graph convolutional networks in electronic health records

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.791081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7f0796ba-9fb6-43a2-bc6e-404b2b96d5de · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification V-net: Fully convolutional neural networks for volumetric medical image segmentation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.869122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation aac01272-c2bc-4f1c-bd41-e26bd1ff74d0 · outbound

This paper cites Focal loss for dense object detection.

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification Focal loss for dense object detection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.810600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a5fc3b36-aea6-4c85-8229-5f4fcccbd8e0 · outbound

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

USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification nnu-net: a self-configuring method for deep learning-based biomedical image segmentation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T08:09:17.859674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

No inbound Pith citation observations are available.