Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T11:30:40.796444Z
Paper Citation Record · LEDGER
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection
As of 23 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2411.18169.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
20 of 20 outbound references displayed
- verified exact0
- verified fuzzy6
- unresolved14
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 7cae0dee-4357-482c-8f29-e3e84ac75ba1 · outbound
This paper cites Surgical endoscopy 26, 3584–3591 (2012).
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection Surgical endoscopy 26, 3584–3591 (2012)
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:30:41.002935Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 532a90db-4ff7-4abe-ab4f-e467a644e375 · outbound
This paper cites In: Med- ical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection In: Med- ical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:30:40.991202Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 22a7798b-dd37-432b-878f-515678957c09 · outbound
This paper cites The International Journal of Robotics Research 43(3), 281–304 (2024).
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection The International Journal of Robotics Research 43(3), 281–304 (2024)
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:30:40.980960Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 82c9e294-b4bf-4d50-980d-b233fd8ebf4f · outbound
This paper cites DINOv2: Learning Robust Visual Features without Supervision.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection DINOv2: Learning Robust Visual Features without Supervision
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:30:40.733707Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b44e67a5-4dcf-450a-8ee3-3a42ebebe856 · outbound
This paper cites LoRA: Low-Rank Adaptation of Large Language Models.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection LoRA: Low-Rank Adaptation of Large Language Models
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:30:40.738543Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 300a4683-2888-4898-be11-f1f6e6c58644 · outbound
This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:30:40.741975Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 81b50ab5-1f32-411d-8f28-fc29ef71da0f · outbound
This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:30:40.962295Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ffec0a72-87a9-4f1b-9be0-0ad6140bac5f · outbound
This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:30:40.749695Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 90dc426e-a72e-4f47-a35f-4afbbac496bb · outbound
This paper cites BMC Medical Imaging 22(1), 109 (2022) Springer Nature 2021 LATEX template 12 PDZSeg: Dissection Zone Segmentation.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection BMC Medical Imaging 22(1), 109 (2022) Springer Nature 2021 LATEX template 12 PDZSeg: Dissection Zone Segmentation
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:30:40.944933Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 68e73ddd-f79b-4b9b-bd4c-c11d81ed3761 · outbound
This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection Rethinking Atrous Convolution for Semantic Image Segmentation
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:30:40.757776Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 69fc4ca8-4c54-4858-9940-d1dcfaf1fe50 · outbound
This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:30:40.762016Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3be74c17-c1ca-4410-89f3-283af9893597 · outbound
This paper cites In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:30:40.765734Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 906e2804-18d4-405b-9eee-25e026770c98 · outbound
This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:30:40.769609Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ddd7d365-3475-4659-a27d-c286e1877af7 · outbound
This paper cites GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:30:40.773483Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f3885580-34dc-437b-b797-67c9572e3dc2 · outbound
This paper cites Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:30:40.777581Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3cfc4d8a-225e-4c8c-9333-f53b91454111 · outbound
This paper cites The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision).
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:30:40.781324Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 27d8e8a9-9796-4dc5-8629-5e8886e25ee9 · outbound
This paper cites ICLR (2021).
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:30:40.784983Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0c8b82bc-813f-4687-b9aa-40e4435d9b08 · outbound
This paper cites In: International Conference on Learning Representations (ICLR), San Diega, CA, USA (2015).
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection In: International Conference on Learning Representations (ICLR), San Diega, CA, USA (2015)
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T11:30:40.914569Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 06efdf5f-3423-4d0e-a371-5df35c2006a8 · outbound
This paper cites Fast-SCNN: Fast Semantic Segmentation Network.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection Fast-SCNN: Fast Semantic Segmentation Network
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:30:40.792103Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 760173b8-d7ab-4bd4-9b85-0f3821b8e6e2 · outbound
This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.
PDZSeg: Adapting the Foundation Model for Dissection Zone Segmentation with Visual Prompts in Robot-assisted Endoscopic Submucosal Dissection Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:30:40.796444Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.