Paper Citation Record · LEDGER

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation

As of 14 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2507.04304.

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

pith.paper-citation-record.v1
2507.04304 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:53:27.855182Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

19 of 19 outbound references displayed

verified exact2
verified fuzzy3
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9bbce4e6-71eb-486a-9a42-c7001924acc5 · outbound

This paper cites Satava, and Alfred Cuschieri, “A systematic review on artificial intelligence in robot-assisted surgery, International Journal of Surgery, vol.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation Satava, and Alfred Cuschieri, “A systematic review on artificial intelligence in robot-assisted surgery, International Journal of Surgery, vol

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:30.096080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 05428756-e0fe-4253-9398-fab9e5f2772f · outbound

This paper cites Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic review,.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic review,

Reference 2

Resolution: verified exact; doi, observed 2026-08-06T19:53:28.116710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fe26dc92-d846-4953-b593-7e851e94c8a5 · outbound

This paper cites an unresolved cited work.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:53:29.903450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 60a11fde-faeb-4cb5-84ff-2b7ce016c947 · outbound

This paper cites Surgical residents’ chal- lenges with the acquisition of surgical skills in operating rooms: A qualitative study,.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation Surgical residents’ chal- lenges with the acquisition of surgical skills in operating rooms: A qualitative study,

Reference 4

Resolution: verified exact; raw_fallback, observed 2026-08-06T19:53:28.961349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c02fcce3-f8af-4757-96c3-09270bbb2448 · outbound

This paper cites AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:26.166721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abb863d5-10b8-4613-863a-54cd3ecc2975 · outbound

This paper cites ISINet: An Instance-Based Approach for Surgical Instrument Segmentation,.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation ISINet: An Instance-Based Approach for Surgical Instrument Segmentation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:29.631626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation afea57e9-a230-4638-a079-c2f956acae6c · outbound

This paper cites SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:26.490931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edec9b52-9cc9-4374-9959-d5ecf34d9267 · outbound

This paper cites TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:26.589093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 676b0710-dc65-49c6-9176-5a4d06e7f123 · outbound

This paper cites an unresolved cited work.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-06T19:53:29.336453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2abd5658-da40-4c24-967b-602bd016e42f · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation,.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation U-Net: Convolutional Networks for Biomedical Image Segmentation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:53:29.151297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 87718d1f-2a8a-4770-afef-eb5f730b0b69 · outbound

This paper cites Mask R-CNN,.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation Mask R-CNN,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:27.011798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18b9810a-a380-4e5b-a5a2-7fd0fcadda44 · outbound

This paper cites Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:27.111313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cbf8c62-1b5e-4e0e-9527-7dbb779d1c57 · outbound

This paper cites Segment Anything.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation Segment Anything

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:27.231991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f02472f-3ca2-4a7d-b23b-daa4e6fbf6b6 · outbound

This paper cites Tversky loss function for image segmentation using 3D fully convolutional deep networks.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation Tversky loss function for image segmentation using 3D fully convolutional deep networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:27.316391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9802d2a-b5e2-4ee9-a2f4-d4c808ddf54e · outbound

This paper cites Cross-Entropy Loss Functions: Theoretical Analysis and Applications.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation Cross-Entropy Loss Functions: Theoretical Analysis and Applications

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:27.446799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e8948c0-1723-4daf-a779-d95226e47cee · outbound

This paper cites SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:27.554513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 884c6d54-fe85-4d2b-a506-d9c422e24908 · outbound

This paper cites 2017 Robotic Instrument Segmentation Challenge.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation 2017 Robotic Instrument Segmentation Challenge

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:27.679283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0125bb3b-913e-46bc-ac20-ffc1f9011010 · outbound

This paper cites 2018 Robotic Scene Segmentation Challenge.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation 2018 Robotic Scene Segmentation Challenge

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:27.855182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2637bc61-3089-49f0-8403-17eb08ff89cd · outbound

This paper cites ISINet: An Instance-Based Approach for Surgical Instrument Segmentation.

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation ISINet: An Instance-Based Approach for Surgical Instrument Segmentation

Reference 2020

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T19:53:28.574919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.