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Paper Citation Record · LEDGER

2017 Robotic Instrument Segmentation Challenge

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 46 inbound Pith citation observations for arXiv:1902.06426.

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

pith.paper-citation-record.v1
1902.06426 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 46 of 46 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 46 of 46 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:13:23.330685Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

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External citation measurements

57
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 47df7afe-eda0-47a6-8363-b4035dccec6c · inbound

Learning Where to Look While Tracking Instruments in Robot-assisted Surgery cites this paper.

Learning Where to Look While Tracking Instruments in Robot-assisted Surgery 2017 Robotic Instrument Segmentation Challenge

Reference 1

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local_arxiv, observed 2026-05-25T13:00:50.079622Z

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source=pdf_text observed=2026-05-25T12:59:35.861020Z digest=sha256:0bc00447454a1f5bc6cd6060f6f5bb339c709d48919001730f250c822f2e17ba

Observation bf16d4b9-2c71-4b11-943f-2ba750ac87c2 · inbound

Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video cites this paper.

Incorporating Temporal Prior from Motion Flow for Instrument Segmentation in Minimally Invasive Surgery Video 2017 Robotic Instrument Segmentation Challenge

Reference 2

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local_arxiv, observed 2026-05-24T20:04:52.789957Z

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source=pdf_text observed=2026-05-24T20:03:07.598018Z digest=sha256:cabd41df272ea0e521637ade40964b2670b7597116aadab42d3032a493129cca

Observation 4f32d42a-47b3-4691-a9ed-a803a8d94745 · inbound

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

U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation 2017 Robotic Instrument Segmentation Challenge

Reference 1

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local_arxiv, observed 2026-05-16T12:36:49.934536Z

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source=pdf_text observed=2026-05-16T12:36:49.861489Z digest=sha256:1444e7e100d2001ab46dcb2adc334bb5c70bce6da24df62d272b29334a434983

Observation c1529776-b030-48a5-874e-2f991d539a11 · inbound

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge cites this paper.

SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge 2017 Robotic Instrument Segmentation Challenge

Reference 25

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local_arxiv, observed 2026-05-23T22:45:50.870783Z

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

source=pdf_text observed=2026-05-23T22:44:36.339252Z digest=sha256:066861f7d94c73b50e195ced4f32431d4404335ab07c088ad1258f4dd1964e02

Observation 9b952b44-34c3-43aa-a3d3-a1d6ed526fc3 · inbound

A Survey of Medical Vision-and-Language Applications and Their Techniques cites this paper.

A Survey of Medical Vision-and-Language Applications and Their Techniques 2017 Robotic Instrument Segmentation Challenge

Reference 218

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source=pdf_text observed=2026-08-12T17:52:02.741505Z digest=sha256:0669591cb25941b2fad9c61378a1db3712e0bfb06572866ccf74e5fb897419a7

Observation 2874e165-5bd5-4a2d-9544-a314ab7055a5 · inbound

Rethinking Text-Promptable Surgical Instrument Segmentation with Robust Framework cites this paper.

Rethinking Text-Promptable Surgical Instrument Segmentation with Robust Framework 2017 Robotic Instrument Segmentation Challenge

Reference 24

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source=pdf_text observed=2026-08-12T17:53:36.812348Z digest=sha256:429a8fba5846baa6e5355d201b81ae0a819922ad3d9be67cf1cb795bb7b04d54

Observation 28b7fa25-d853-49d2-9e43-7e1dd68d4f6e · inbound

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline cites this paper.

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline 2017 Robotic Instrument Segmentation Challenge

Reference 11

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source=pdf_text observed=2026-08-12T17:14:08.386897Z digest=sha256:9652f140938d71ce52112df52620b69e3654a8de6a3990fdca63729b0a0484a9

Observation b566df68-052d-4ff7-902c-ba067650ebe4 · inbound

Expanded Comprehensive Robotic Cholecystectomy Dataset (CRCD) cites this paper.

Expanded Comprehensive Robotic Cholecystectomy Dataset (CRCD) 2017 Robotic Instrument Segmentation Challenge

Reference 2019

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source=pdf_text observed=2026-08-11T14:24:16.611213Z digest=sha256:4250b3239282913df0a26d87a917dfa09e2da1be12179097e2ba01bcf5d7dfe0

Observation 57595aeb-0591-4911-ae87-77910e107367 · inbound

Laparoscopic Scene Analysis for Intraoperative Visualisation of Gamma Probe Signals in Minimally Invasive Cancer Surgery cites this paper.

Laparoscopic Scene Analysis for Intraoperative Visualisation of Gamma Probe Signals in Minimally Invasive Cancer Surgery 2017 Robotic Instrument Segmentation Challenge

Reference 52

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source=pdf_text observed=2026-08-10T22:25:45.609634Z digest=sha256:a5ad1187772b6d77dbaaaccd21c87b3a505e8945ff33e74108d2d8141f9de8fa

Observation c5ccd95c-5041-4120-b772-d9edb74fdc25 · inbound

SurgRIPE challenge: Benchmark of Surgical Robot Instrument Pose Estimation cites this paper.

SurgRIPE challenge: Benchmark of Surgical Robot Instrument Pose Estimation 2017 Robotic Instrument Segmentation Challenge

Reference 5

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source=arxiv_source observed=2026-08-10T22:05:11.677980Z digest=sha256:76b47831060b652758990aff4965ac6eebfda524df19c5889baf8505566805fe

Observation 2d835fd9-8de0-4058-acfa-20105d5c0ab4 · inbound

Imbalanced Medical Image Segmentation with Pixel-dependent Noisy Labels cites this paper.

Imbalanced Medical Image Segmentation with Pixel-dependent Noisy Labels 2017 Robotic Instrument Segmentation Challenge

Reference 33

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source=pdf_text observed=2026-08-10T21:00:06.247344Z digest=sha256:19942cc52dd40484cc4faf9e5d09d05fa31b7fcefb8da049648a6d3d003b1d09

Observation c740cc5a-6e51-4420-b62c-f1dc7a2fd408 · inbound

SimGen: A Diffusion-Based Framework for Simultaneous Surgical Image and Segmentation Mask Generation cites this paper.

SimGen: A Diffusion-Based Framework for Simultaneous Surgical Image and Segmentation Mask Generation 2017 Robotic Instrument Segmentation Challenge

Reference 14

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source=pdf_text observed=2026-08-10T20:16:11.974798Z digest=sha256:6c6198cfc2c18d922987ee9d544be090ec7c3c8624e2457a3147e4fe7a754f7f

Observation 9610c571-ccc4-4d1f-8a52-15797a4aa1c2 · inbound

Surgical Visual Understanding (SurgVU) Dataset cites this paper.

Surgical Visual Understanding (SurgVU) Dataset 2017 Robotic Instrument Segmentation Challenge

Reference 12

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local_arxiv, observed 2026-05-23T06:02:38.123916Z

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

source=pdf_text observed=2026-05-23T05:58:32.582946Z digest=sha256:dbae08698ab2c519fc00968942fbe31ef64d42b2b4339ce55937fb07801bbacd

Observation a69a01d3-ed9a-499a-8de3-7989a1c18fdb · inbound

EndoChat: Grounded Multimodal Large Language Model for Endoscopic Surgery cites this paper.

EndoChat: Grounded Multimodal Large Language Model for Endoscopic Surgery 2017 Robotic Instrument Segmentation Challenge

Reference 5

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source=pdf_text observed=2026-08-10T18:24:41.711045Z digest=sha256:a13c6503e2c3016a67b45edd33edf4e832c14c53403ea999fd425ec2af57c69f

Observation 7ea4ecf4-d277-442a-add6-e01113fc7677 · inbound

Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data cites this paper.

Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data 2017 Robotic Instrument Segmentation Challenge

Reference 2022

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source=pdf_text observed=2026-08-10T14:27:14.088840Z digest=sha256:7ff1f58df8532e5a046a87637d836df9a216397eeb5ef194e1a0a24cf76d91d9

Observation d37e154c-4c92-4aef-91e8-43a4a9943880 · inbound

StereoMamba: Real-time and Robust Intraoperative Stereo Disparity Estimation via Long-range Spatial Dependencies cites this paper.

StereoMamba: Real-time and Robust Intraoperative Stereo Disparity Estimation via Long-range Spatial Dependencies 2017 Robotic Instrument Segmentation Challenge

Reference 27

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local_arxiv, observed 2026-05-22T18:46:57.635886Z

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

source=pdf_text observed=2026-05-22T18:45:23.171409Z digest=sha256:9e7900d127e7cd9bdfd7cfd50b7d7558c002029b9bbccb82885f7a4c7dc139a9

Observation 9f35dae7-899d-4a9e-85c0-ad3ee4da2b32 · inbound

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation cites this paper.

Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation 2017 Robotic Instrument Segmentation Challenge

Reference 1

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source=pdf_text observed=2026-08-15T22:13:23.330685Z digest=sha256:4c133ac3672f6dbe1e81d57b943a955cfc4ad52fe4bbfe1518437920740c4779

Observation b7a97220-dce9-4a8d-8618-e17c41c87c32 · inbound

ReSurgSAM2: Referring Segment Anything in Surgical Video via Credible Long-term Tracking cites this paper.

ReSurgSAM2: Referring Segment Anything in Surgical Video via Credible Long-term Tracking 2017 Robotic Instrument Segmentation Challenge

Reference 2

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source=pdf_text observed=2026-08-15T21:55:33.859458Z digest=sha256:a54d8a03e6956849a791da28bbcbf4d8d9a2c3d9c4500cf257d18da0d1b22352

Observation 0707f4b9-e643-47f3-9223-55fba8d16640 · inbound

Recent Advances in Medical Imaging Segmentation: A Survey cites this paper.

Recent Advances in Medical Imaging Segmentation: A Survey 2017 Robotic Instrument Segmentation Challenge

Reference 102

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source=pdf_text observed=2026-08-15T21:38:10.697085Z digest=sha256:e6af68a615a1f0cfbeec3f7dce025a1503d87ba76b5daf89cdc260a132ed1f5d

Observation b1425172-aeeb-4e22-9caa-bf58372e21d2 · inbound

MedSG-Bench: A Benchmark for Medical Image Sequences Grounding cites this paper.

MedSG-Bench: A Benchmark for Medical Image Sequences Grounding 2017 Robotic Instrument Segmentation Challenge

Reference 5

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source=pdf_text observed=2026-08-15T20:50:47.410075Z digest=sha256:f8a7bed690dad406cc7e7b87bc7e396d9f8823a10fc083a44b080ad1fa0f6620

Observation 3ec56a87-766c-4818-9970-36a05edbb4d6 · inbound

Are Vision Language Models Ready for Clinical Diagnosis? A 3D Medical Benchmark for Tumor-centric Visual Question Answering cites this paper.

Are Vision Language Models Ready for Clinical Diagnosis? A 3D Medical Benchmark for Tumor-centric Visual Question Answering 2017 Robotic Instrument Segmentation Challenge

Reference 5

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source=pdf_text observed=2026-08-07T14:26:28.744200Z digest=sha256:2da18ae65e2e3001b6d5d25527976436ffb1a7887d2a24e073978a32510266dd

Observation b90920a1-3940-4c9a-b1e2-072359f503ec · inbound

SurgVLM: A Large Vision-Language Model and Systematic Evaluation Benchmark for Surgical Intelligence cites this paper.

SurgVLM: A Large Vision-Language Model and Systematic Evaluation Benchmark for Surgical Intelligence 2017 Robotic Instrument Segmentation Challenge

Reference 4

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source=pdf_text observed=2026-08-07T11:26:37.402107Z digest=sha256:f8cad5c4691da232bea18e75f713198faf2211f2df0a1f1418148fd9bbe1fb36

Observation ce31a7b5-f247-4aa0-a333-33539f31a7b2 · inbound

EndoARSS: Adapting Spatially-Aware Foundation Model for Efficient Activity Recognition and Semantic Segmentation in Endoscopic Surgery cites this paper.

EndoARSS: Adapting Spatially-Aware Foundation Model for Efficient Activity Recognition and Semantic Segmentation in Endoscopic Surgery 2017 Robotic Instrument Segmentation Challenge

Reference 67

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source=pdf_text observed=2026-08-07T05:52:28.433569Z digest=sha256:925df5393613778460adb254e828b2660a67cb2392876bf0836e03115df962f0

Observation 30b3032c-54da-4f24-9c6c-e35bcac52831 · inbound

Surgery-R1: Advancing Surgical-VQLA with Reasoning Multimodal Large Language Model via Reinforcement Learning cites this paper.

Surgery-R1: Advancing Surgical-VQLA with Reasoning Multimodal Large Language Model via Reinforcement Learning 2017 Robotic Instrument Segmentation Challenge

Reference 40

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source=pdf_text observed=2026-08-06T23:12:12.826059Z digest=sha256:a1d7f06bd816c0cd43589e54c42a22231433cfb280fe4c170b8187c90b99c15b

Observation 9d7a2bb8-ce66-41f1-900d-acef04c4d422 · inbound

SurgiSR4K: A High-Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures cites this paper.

SurgiSR4K: A High-Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures 2017 Robotic Instrument Segmentation Challenge

Reference 1

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local_arxiv, observed 2026-05-19T06:22:07.778955Z

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source=pdf_text observed=2026-05-19T06:19:46.518422Z digest=sha256:29aefae50338c02c61a4df7409c631655354f8d4f6f00bad667707d8ad4902a5

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

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation cites this paper.

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

Reference 17

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source=pdf_text observed=2026-08-06T19:53:27.679283Z digest=sha256:55c69ac20a0d0a214e0b95df2a0fc29d49102a9368f19119ff9cc2e21bec9883

Observation 2d750b7f-142f-4b80-ab8d-c9fcc45cab0f · inbound

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning cites this paper.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning 2017 Robotic Instrument Segmentation Challenge

Reference 44

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source=pdf_text observed=2026-08-06T19:54:39.650058Z digest=sha256:92b3c22d85e56325d056110df77fc3832fddb6f9774df428290ead1883012836

Observation 61eefab6-4cc2-43eb-99dc-076733a6be0c · inbound

Learning from Sparse Point Labels for Dense Carcinosis Localization in Advanced Ovarian Cancer Assessment cites this paper.

Learning from Sparse Point Labels for Dense Carcinosis Localization in Advanced Ovarian Cancer Assessment 2017 Robotic Instrument Segmentation Challenge

Reference 1

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source=pdf_text observed=2026-08-06T19:03:41.444223Z digest=sha256:6211720ebe5fd5a753e8d8a7c430c47c21052dd7a8c359b34770e4f9f9d01094

Observation 1955fe34-18e9-4a0c-80a1-20ff04ae13db · inbound

Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation cites this paper.

Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation 2017 Robotic Instrument Segmentation Challenge

Reference 2

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source=pdf_text observed=2026-08-06T17:58:44.964081Z digest=sha256:088acc0c5f99d3b3f56fb161f8f948ed0af1dcf002135bba3e59b3e9ea695d53

Observation 69b45db8-6a3b-4cc4-b54d-c2ef51a756b2 · inbound

Beyond Rigid AI: Towards Natural Human-Machine Symbiosis for Interoperative Surgical Assistance cites this paper.

Beyond Rigid AI: Towards Natural Human-Machine Symbiosis for Interoperative Surgical Assistance 2017 Robotic Instrument Segmentation Challenge

Reference 2

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source=pdf_text observed=2026-08-06T11:08:34.724570Z digest=sha256:dc1b1f62670b6136aaf79f7dc80d0b4126d98331e9caccd66997d328b6b318cf

Observation c57b53bc-8812-44d7-9203-78b558f853bd · inbound

Dynamic Robot-Assisted Surgery with Hierarchical Class-Incremental Semantic Segmentation cites this paper.

Dynamic Robot-Assisted Surgery with Hierarchical Class-Incremental Semantic Segmentation 2017 Robotic Instrument Segmentation Challenge

Reference 1

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source=pdf_text observed=2026-08-06T05:31:31.597312Z digest=sha256:56fb59721c57a47bf910cf9f7ea3e4c36214b205422e219061f31fe7ac660785

Observation 05417bc0-0e9b-43da-b06c-59386e1d3d5e · inbound

MetaScope: Optics-Driven Neural Network for Ultra-Micro Metalens Endoscopy cites this paper.

MetaScope: Optics-Driven Neural Network for Ultra-Micro Metalens Endoscopy 2017 Robotic Instrument Segmentation Challenge

Reference 4

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source=arxiv_source observed=2026-08-06T04:24:17.991266Z digest=sha256:b34271b0accbbd30ae29e700080f1cac77c29989b133fc04bf16530a9ef5551e

Observation cd41ac32-1df3-4e41-af6d-4d5d755562fb · inbound

SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking cites this paper.

SurgSLOT: Segment Anything in Surgical Videos via Semantic Long-term Tracking 2017 Robotic Instrument Segmentation Challenge

Reference 5

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source=pdf_text observed=2026-08-03T21:12:03.135925Z digest=sha256:787d09940a044c1ddd22e33133f7f368537086f8f4fef90ca88301386a487d16

Observation 77c2c0f2-6bb4-446e-9ad9-f8ee96dcf127 · inbound

S2M-Net: Spectral-Spatial Mixing for Medical Image Segmentation with Morphology-Aware Adaptive Loss cites this paper.

S2M-Net: Spectral-Spatial Mixing for Medical Image Segmentation with Morphology-Aware Adaptive Loss 2017 Robotic Instrument Segmentation Challenge

Reference 40

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local_arxiv, observed 2026-05-16T17:31:08.401278Z

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

source=pdf_text observed=2026-05-16T17:29:54.076300Z digest=sha256:75e3985caaf7884c2d233518cd25e9e97fa1e2214e7a1b852af87234b7839c15

Observation 4f49fc4d-7750-412c-b2bd-c03b69e80bbf · inbound

M3CoTBench: Benchmark Chain-of-Thought of MLLMs in Medical Image Understanding cites this paper.

M3CoTBench: Benchmark Chain-of-Thought of MLLMs in Medical Image Understanding 2017 Robotic Instrument Segmentation Challenge

Reference 102

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source=pdf_text observed=2026-08-03T10:49:59.078902Z digest=sha256:d94a640d957cefed078f3d41819daa158a7d0649041d5db6b70a28495bd91ae7

Observation 02ea951c-c6aa-4513-a77a-7cbb3a02450f · inbound

Unlocking Positive Transfer in Incrementally Learning Surgical Instruments: A Self-reflection Hierarchical Prompt Framework cites this paper.

Unlocking Positive Transfer in Incrementally Learning Surgical Instruments: A Self-reflection Hierarchical Prompt Framework 2017 Robotic Instrument Segmentation Challenge

Reference 1

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arxiv_id, observed 2026-05-13T21:03:20.391519Z

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.

source=pdf_text observed=2026-05-13T20:58:29.515826Z digest=sha256:aa440279a1a404428de343e24b72a61e7a50252c9a448e2328bdfc4f5f1a0f29

Observation b7e9a2c6-fc68-4dd1-938c-12cd0817c785 · inbound

Benchmarking CNN- and Transformer-Based Models for Surgical Instrument Segmentation in Robotic-Assisted Surgery cites this paper.

Benchmarking CNN- and Transformer-Based Models for Surgical Instrument Segmentation in Robotic-Assisted Surgery 2017 Robotic Instrument Segmentation Challenge

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:21:00.921823Z

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.

source=pdf_text observed=2026-05-10T17:40:08.036557Z digest=sha256:b71df55212663a9989b15e948999e1ed288a798ad5fcf0237bd94a993bcae33e

Observation 4d2d1dc4-2d0f-4567-b42f-4ebea7075f87 · inbound

Attention Is not Everything: Efficient Alternatives for Vision cites this paper.

Attention Is not Everything: Efficient Alternatives for Vision 2017 Robotic Instrument Segmentation Challenge

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-10T05:51:10.457997Z

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.

source=pdf_text observed=2026-05-10T05:46:33.053449Z digest=sha256:c6e9ccda1702abff9acde868e8261a7324f3af9a770b181427f9d082ca69b1f3

Observation 92b44ae8-33ec-4425-b80a-90b7083000e4 · inbound

USEMA: a Scalable Efficient Mamba Like Attention for Medical Image Segmentation cites this paper.

USEMA: a Scalable Efficient Mamba Like Attention for Medical Image Segmentation 2017 Robotic Instrument Segmentation Challenge

Reference 1

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T07:17:29.601895Z

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.

source=pdf_text observed=2026-05-13T07:13:37.134769Z digest=sha256:e6c5708a6e0fdcc4598b8116491a08f9d9c5db1a630255c735505116218352fe

Observation d6008e62-a91e-4542-9d80-d5c3ebc10571 · inbound

RoboSurg-VQA: A Multimodal Benchmark for Surgical Segmentation-Aware Visual Question Answering cites this paper.

RoboSurg-VQA: A Multimodal Benchmark for Surgical Segmentation-Aware Visual Question Answering 2017 Robotic Instrument Segmentation Challenge

Reference 4

Resolution
metadata mismatch
local_arxiv, observed 2026-05-25T05:25:23.532623Z

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.

source=pdf_text observed=2026-05-25T05:22:44.481868Z digest=sha256:3f58298480947e4fc6827ea43a43389722a68dd366d4349241e5157259c1ded1

Observation bc93a483-0fff-4de2-9053-e235e85f27a4 · inbound

Surgical Anatomy Recognition with Context Learning using Foundation Representations cites this paper.

Surgical Anatomy Recognition with Context Learning using Foundation Representations 2017 Robotic Instrument Segmentation Challenge

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-07-04T07:59:40.025386Z

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.

source=pdf_text observed=2026-06-26T12:23:49.014408Z digest=sha256:c8a051424fc96a8f19503583c5f24353e38f542807420dd99d0977d993f7fb2c

Observation abc199bc-bb58-4af6-b96a-2e01a9bc7cb9 · inbound

SurgAtlas: A Large-Scale Surgical Video-Language Dataset with 2,391 Hours of Open and Minimally Invasive Surgery cites this paper.

SurgAtlas: A Large-Scale Surgical Video-Language Dataset with 2,391 Hours of Open and Minimally Invasive Surgery 2017 Robotic Instrument Segmentation Challenge

Reference 27

Resolution
verified exact
local_arxiv, observed 2026-07-04T20:10:07.454301Z

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.

source=pdf_text observed=2026-06-25T20:39:21.354834Z digest=sha256:9e4702a1a3af80b615a560ea94ff5d77418f603527fddcfa23ed21b62b1720e0

Observation 5c100966-3a4d-4fe1-9fa5-55fc33e74d9d · inbound

Dense Structural Priors for Sparse Functional Landmark Localization in Surgical Videos cites this paper.

Dense Structural Priors for Sparse Functional Landmark Localization in Surgical Videos 2017 Robotic Instrument Segmentation Challenge

Reference 1

Resolution
metadata mismatch
local_arxiv, observed 2026-07-01T00:55:11.527736Z

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.

source=pdf_text observed=2026-07-01T00:45:35.189844Z digest=sha256:5418299dc25701aa412013ef4c1d950ba7ff2ecef4454504e86d741633702efe

Observation 89f35159-23f7-42ae-bf3b-8f706438b4d1 · inbound

HSMLA: Hierarchical Softmax Multi-scale Linear Attention for Efficient Vision Transformers cites this paper.

HSMLA: Hierarchical Softmax Multi-scale Linear Attention for Efficient Vision Transformers 2017 Robotic Instrument Segmentation Challenge

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-11T00:33:23.426911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T00:33:23.426911Z digest=sha256:1b98584b7503069f0367fd28155bc2829ccd555adf33ff73c04ac7d664838976

Observation e73c90f0-9509-4559-add3-76fa92d0ea77 · inbound

HSMLA: Hierarchical Softmax Multi-scale Linear Attention for Efficient Vision Transformers cites this paper.

HSMLA: Hierarchical Softmax Multi-scale Linear Attention for Efficient Vision Transformers 2017 Robotic Instrument Segmentation Challenge

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-15T14:32:47.254653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:32:47.254653Z digest=sha256:36c96bb2533723e20eba9b6f94dec03712c0ac2130b38a479e9fbd0709aa1809

Observation c7e17336-ebba-4a06-bb82-15b9fc4b7ceb · inbound

Toward Mask Annotation-Free Surgical Instrument Segmentation from Endoscopic Images Using Text-Prompted Segment Anything Model 3 (SAM3) cites this paper.

Toward Mask Annotation-Free Surgical Instrument Segmentation from Endoscopic Images Using Text-Prompted Segment Anything Model 3 (SAM3) 2017 Robotic Instrument Segmentation Challenge

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-14T04:28:09.997629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:28:09.997629Z digest=sha256:9eabd96e40a6265d95016fa3209b45508901c1be2f96df9467ba65987a79fd4c