Paper Citation Record · LEDGER

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation

As of 18 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2607.29509.

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

pith.paper-citation-record.v1
2607.29509 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T05:41:18.082976Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

31 of 31 outbound references displayed

verified exact1
verified fuzzy0
unresolved29
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 398e982a-897c-4e39-bf73-b3aa22986422 · outbound

This paper cites Vision techniques for anatomical structures in laparoscopic surgery: a comprehen- sive review.Frontiers in Surgery, 12:1557153, 2025.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Vision techniques for anatomical structures in laparoscopic surgery: a comprehen- sive review.Frontiers in Surgery, 12:1557153, 2025

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:15.718433Z

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Unavailable: canonical work link unavailable.

Observation aabe6728-73af-4c02-82f6-a22584265615 · outbound

This paper cites an unresolved cited work.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:15.778968Z

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Unavailable: canonical work link unavailable.

Observation 3213c4ee-8d18-4ebf-a2ad-48ed4234ed34 · outbound

This paper cites Deep learning for surgical instrument recognition and segmentation in robotic- assisted surgeries: a systematic review.Artificial Intelligence Review, 58(1):1, 2024.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Deep learning for surgical instrument recognition and segmentation in robotic- assisted surgeries: a systematic review.Artificial Intelligence Review, 58(1):1, 2024

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:15.903154Z

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Observation 891e6c8c-f083-4a67-8204-48f030e3c7eb · outbound

This paper cites Augmenting efficient real-time surgical instrument segmentation in video with point tracking and segment anything.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Augmenting efficient real-time surgical instrument segmentation in video with point tracking and segment anything

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:15.955082Z

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Unavailable: canonical work link unavailable.

Observation b0c11077-3c61-4af4-b4cb-c98f5c3275d6 · outbound

This paper cites Segmatch: semi-supervised surgical instrument segmentation.Scientific Reports, 15(1):14042, 2025.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Segmatch: semi-supervised surgical instrument segmentation.Scientific Reports, 15(1):14042, 2025

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:16.015709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2856f17d-4e57-4991-a379-367906f2cdcd · outbound

This paper cites an unresolved cited work.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Unresolved cited work

Reference 6

Resolution: malformed identifier; no resolver link, observed 2026-08-03T05:41:16.107419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25f28c15-e0cc-4b6a-b5f6-65829a19d2a4 · outbound

This paper cites Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation, 2023.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation, 2023

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:16.193212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8429c37f-c6f2-4cef-a21f-cdc59526c078 · outbound

This paper cites Mosmos: Multi-organ segmentation facilitated by medical report supervision.Biomedical Signal Processing and Control, 106:107743, 2025.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Mosmos: Multi-organ segmentation facilitated by medical report supervision.Biomedical Signal Processing and Control, 106:107743, 2025

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:16.263456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e8e8a80-a6e8-4974-9de3-a4cc0d28b7e8 · outbound

This paper cites M¨ uller-Stich, Martin Wagner, and Franziska Mathis-Ullrich.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation M¨ uller-Stich, Martin Wagner, and Franziska Mathis-Ullrich

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:16.338304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56ac9c0e-6ed6-4e30-ad9c-c584ec6196c2 · outbound

This paper cites Evolution of multiorgan segmentation techniques from traditional to deep learning in abdominal ct images – a systematic review.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Evolution of multiorgan segmentation techniques from traditional to deep learning in abdominal ct images – a systematic review

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:16.442585Z

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Unavailable: canonical work link unavailable.

Observation a73b5625-88da-4172-8ece-6fd1b224b5df · outbound

This paper cites A review of deep learning based methods for medical image multi-organ segmentation.Physica Medica, 85:107–122, 2021.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation A review of deep learning based methods for medical image multi-organ segmentation.Physica Medica, 85:107–122, 2021

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:16.539101Z

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Observation 67fbe92a-c6da-477e-aa65-75edec241b27 · outbound

This paper cites CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:16.666284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7922c77-d024-437c-a9af-08104a1533a3 · outbound

This paper cites The dresden surgical anatomy dataset for abdominal organ segmentation in surgical data science.Scientific Data, 10, 01 2023.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation The dresden surgical anatomy dataset for abdominal organ segmentation in surgical data science.Scientific Data, 10, 01 2023

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:16.807032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd944f07-1e27-4526-8a0e-2377c43e3f8a · outbound

This paper cites Strategies to improve real-world applicability of laparoscopic anatomy segmentation models.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Strategies to improve real-world applicability of laparoscopic anatomy segmentation models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:16.897797Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1ec4f6b-f2fb-4520-b3ed-01d3b3412bc8 · outbound

This paper cites Kolbinger, Franziska M.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Kolbinger, Franziska M

Reference 15

Resolution: verified exact; doi, observed 2026-08-03T05:44:13.987285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a1289b49-7a6f-4ec0-b3d9-c5799a42fb89 · outbound

This paper cites One model to use them all: training a segmentation model with com- plementary datasets.International journal of computer assisted radiology and surgery, 19(6): 1233–1241, 2024.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation One model to use them all: training a segmentation model with com- plementary datasets.International journal of computer assisted radiology and surgery, 19(6): 1233–1241, 2024

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:17.092434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d60c8e4c-c09d-439d-94dd-b4cb5e1df3df · outbound

This paper cites Effective disjoint representational learning for anatomical segmentation.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Effective disjoint representational learning for anatomical segmentation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:17.170427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aeb7a166-f760-457a-953e-a067e4b867fb · outbound

This paper cites Efficient anatomy segmentation in laparoscopic surgery using multi-teacher knowledge distillation.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Efficient anatomy segmentation in laparoscopic surgery using multi-teacher knowledge distillation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:17.252910Z

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Unavailable: canonical work link unavailable.

Observation 58870727-51c9-49e3-ae82-b42daa2a3429 · outbound

This paper cites Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ seg- mentation.BioMedical Engineering OnLine, 23(1):52, 2024.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ seg- mentation.BioMedical Engineering OnLine, 23(1):52, 2024

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:17.304587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81aad27b-3e4d-441d-996a-ce66c84c1256 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:17.380124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e51e5a6-6b23-4d42-84b7-1e8bccfff593 · outbound

This paper cites Advancements and challenges in medical image segmentation: A comprehensive survey.Sensors and AI, pages 3–29, 2025.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Advancements and challenges in medical image segmentation: A comprehensive survey.Sensors and AI, pages 3–29, 2025

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:17.437452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbfa4a1b-3dfd-4d58-b7e3-3fc578b80914 · outbound

This paper cites Improving surgical scene seman- tic segmentation through a deep learning architecture with attention to class imbalance.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Improving surgical scene seman- tic segmentation through a deep learning architecture with attention to class imbalance

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:17.550324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c82179e-bd8a-4133-bde7-4b1ec9e9a959 · outbound

This paper cites Warfield, and Ali Gholipour.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Warfield, and Ali Gholipour

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:17.635599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17a13da0-7d9c-4378-a4ca-2d9699d634bb · outbound

This paper cites Curran Associates Inc., Red Hook, NY, USA, 2019.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Curran Associates Inc., Red Hook, NY, USA, 2019

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:17.687898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 904feb08-7b67-4ea9-90da-e4d1ff1afd8f · outbound

This paper cites Critical assessment of transfer learning for medical image segmentation with fully convolutional neural networks, 05 2020.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Critical assessment of transfer learning for medical image segmentation with fully convolutional neural networks, 05 2020

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:17.760087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12530971-3df9-4930-b4b8-3ce4aac5bd6b · outbound

This paper cites Jumpstarting surgical computer vision.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Jumpstarting surgical computer vision

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:17.822389Z

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Observation 9c545678-258f-4874-8d28-c4da50bf6ed9 · outbound

This paper cites Jaspers, Ronald L.P.D.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Jaspers, Ronald L.P.D

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:17.880919Z

Source-reported events for the cited work

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Observation f348e2b4-3aa2-4469-be52-06cf66b82d97 · outbound

This paper cites Efficient generative-adversarial u-net for multi-organ medical image segmentation.Journal of Imaging, 11(1):19, 2025.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Efficient generative-adversarial u-net for multi-organ medical image segmentation.Journal of Imaging, 11(1):19, 2025

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:17.925163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c766cc00-e1fa-440b-8230-0419b274e3d9 · outbound

This paper cites A unified loss for handling inter-class and intra-class imbalance in medical image segmentation.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation A unified loss for handling inter-class and intra-class imbalance in medical image segmentation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:17.979468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6850e1a2-abd9-4e6b-854a-f28c2058297b · outbound

This paper cites Endonet: a deep architecture for recognition tasks on laparoscopic videos.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Endonet: a deep architecture for recognition tasks on laparoscopic videos

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:18.037080Z

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Unavailable: canonical work link unavailable.

Observation db85f593-7130-417d-a67f-9248ef11f43e · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentation Attention U-Net: Learning Where to Look for the Pancreas

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T05:41:18.082976Z

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Pith citing papers

No inbound Pith citation observations are available.