Paper Citation Record · LEDGER

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation

As of 15 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2608.08191.

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

pith.paper-citation-record.v1
2608.08191 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:22:31.767360Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

40 of 40 outbound references displayed

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verified fuzzy38
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7099c7e9-19b7-493b-a398-63086b9dec17 · outbound

This paper cites Ultrasound image segment ation: a survey,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Ultrasound image segment ation: a survey,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.228770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d13f8ccc-24a1-449d-aa86-8e4c6df9ee42 · outbound

This paper cites Male pelvic multi-organ segmentation on transrectal ultrasound using anchor-free mask cnn,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Male pelvic multi-organ segmentation on transrectal ultrasound using anchor-free mask cnn,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.217481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation efa68728-34fd-446a-8788-de361331915c · outbound

This paper cites Deep-learning-based multi-organ auto-segmentation on 3d transrectal ultrasound for ultrasound-guided prostate brachytherapy,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Deep-learning-based multi-organ auto-segmentation on 3d transrectal ultrasound for ultrasound-guided prostate brachytherapy,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.206745Z

Source-reported events for the cited work

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Observation e4c1862e-745d-4f7a-8a53-62867cb48939 · outbound

This paper cites Pfus1: Premier pelvic ﬂoor ultrasound segmenta- tion dataset. a resource for advancing research.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Pfus1: Premier pelvic ﬂoor ultrasound segmenta- tion dataset. a resource for advancing research

Reference 4

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Observation f200a303-f0de-4e05-907a-7d178a60ee5e · outbound

This paper cites Metrics for evaluating 3d medi cal image segmentation: analysis, selection, and tool,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Metrics for evaluating 3d medi cal image segmentation: analysis, selection, and tool,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.186113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation eca173b4-8f3f-4756-a424-208e72e72af2 · outbound

This paper cites Family of boundary overlap metrics for the evaluation of medical image segmentation,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Family of boundary overlap metrics for the evaluation of medical image segmentation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.175779Z

Source-reported events for the cited work

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Observation d9880f24-e688-405a-9f89-6b0e5d5aadaa · outbound

This paper cites Gec-estro acrop prostate brachytherapy guidelines,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Gec-estro acrop prostate brachytherapy guidelines,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.163997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4d0c0a7c-5085-459e-a56b-cb5dc4334eda · outbound

This paper cites Ronneberger, P.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Ronneberger, P

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.153499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ee81ff0c-3918-420e-a397-831f8fa6588c · outbound

This paper cites Milletari, N.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Milletari, N

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.142363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0ac0cb37-0233-4a3a-8045-36b85e8ffc56 · outbound

This paper cites nnu-net,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation nnu-net,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.131198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b819c27a-acb2-4950-b1eb-5446068314da · outbound

This paper cites Boundary loss for highly unbalanced segmentation,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Boundary loss for highly unbalanced segmentation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.119646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 32abdf11-5d83-416d-9c08-149795b2a4d5 · outbound

This paper cites Reducing the hausdorff d istance in medical image segmentation with convolutional neural netw orks,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Reducing the hausdorff d istance in medical image segmentation with convolutional neural netw orks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.108923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 648189bd-daf7-46f8-8dc1-2bf51b7eb6dc · outbound

This paper cites Segment anything,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Segment anything,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.099343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 434e5ca1-2659-404c-9c54-43a5537a5ead · outbound

This paper cites Segment anything model for medical image analysis: an experimental study,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Segment anything model for medical image analysis: an experimental study,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.088539Z

Source-reported events for the cited work

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Observation cf1e305d-3d33-4539-8c20-1116ef0c6dc3 · outbound

This paper cites Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets

Reference 15

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Source-reported events for the cited work

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Observation aae0a8f4-426c-4491-8e49-49641d3091bb · outbound

This paper cites Finite-time analysis of the multiarmed bandit problem,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Finite-time analysis of the multiarmed bandit problem,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.077005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aeb71c26-4a31-4e45-aedb-bba004b4b7b4 · outbound

This paper cites Segment anything in medical images,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Segment anything in medical images,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.066308Z

Source-reported events for the cited work

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Observation f6c7b85a-7456-44d3-9887-0c2a767a01b3 · outbound

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

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.055799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 75fa89bb-30b5-40a7-98c1-93e2554db021 · outbound

This paper cites Hfa-unet,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Hfa-unet,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.044382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a74a7a41-0cf8-4046-a945-495f397ddf75 · outbound

This paper cites Microsegnet,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Microsegnet,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.034833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f74ee28b-7df0-4d93-8559-0f1aa5ff165a · outbound

This paper cites U-net benign prostatic hyperplasia-trained deep learni ng model for prostate ultrasound image segmentation in prosta te cancer,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation U-net benign prostatic hyperplasia-trained deep learni ng model for prostate ultrasound image segmentation in prosta te cancer,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.023083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 55720d40-57ad-4934-95f1-c99494b1db97 · outbound

This paper cites Automated segmentation and measurement of the female pelvic ﬂoor from the mid-sagittal plane of 3d ultr asound volumes,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Automated segmentation and measurement of the female pelvic ﬂoor from the mid-sagittal plane of 3d ultr asound volumes,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.011955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9d3056aa-3658-4473-a4ea-3ce4914abc7e · outbound

This paper cites Measures of the amount of ecologic associat ion between species,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Measures of the amount of ecologic associat ion between species,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:32.001043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a1214b4f-a150-4f0c-863f-bdce51bfb4f6 · outbound

This paper cites Comparison and evaluation of methods for liver segmentation from ct datasets,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Comparison and evaluation of methods for liver segmentation from ct datasets,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.989117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 15ef91e2-d579-4468-9e01-0d27d43f9ec8 · outbound

This paper cites Comparing images using the hausdorff distance,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Comparing images using the hausdorff distance,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.977835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation af0a9ad8-1e9e-45f9-a660-e7c768894593 · outbound

This paper cites Weakmedsam,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Weakmedsam,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.966329Z

Source-reported events for the cited work

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Observation ffd620ca-1544-4f0f-8ee8-abf91a453949 · outbound

This paper cites Acea-net: Weakly supervised prostate 3d mri image segmentation via advanced prompt points,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Acea-net: Weakly supervised prostate 3d mri image segmentation via advanced prompt points,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.955927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c2c9f43b-bbfb-4760-8160-a77f845efbc8 · outbound

This paper cites Sam2rad,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Sam2rad,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.944773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 16643621-4d7c-428d-a864-0996787b5471 · outbound

This paper cites Autoprosam: Automated prompting sam for 3d multi-organ segmentation,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Autoprosam: Automated prompting sam for 3d multi-organ segmentation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.933584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 065e233a-1171-40f8-9db2-5e7a23738c6f · outbound

This paper cites Alignsam,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Alignsam,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.922089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5948f083-6d85-49dc-963e-5bae70654aec · outbound

This paper cites Plug-and-play ppo,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Plug-and-play ppo,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.910368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b9a4de51-a624-46ab-81aa-9ef3e07a18d9 · outbound

This paper cites Temporally-extended prompts optimization for sam in interactive medical image segmentation,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Temporally-extended prompts optimization for sam in interactive medical image segmentation,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.898934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3bb9670e-d9d2-48f1-a2fe-07f60dd78115 · outbound

This paper cites Optimizing efﬁciency and effectiveness in sequential prompt strategy for sam using reinforcement learning,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Optimizing efﬁciency and effectiveness in sequential prompt strategy for sam using reinforcement learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.887480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 918aae1d-ee41-454c-86fa-715a612c4653 · outbound

This paper cites On upper-conﬁdence bound policies for switching bandit problems,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation On upper-conﬁdence bound policies for switching bandit problems,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.875932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e674540c-bc51-4211-9942-efffb93714fc · outbound

This paper cites Batched bandit problems,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Batched bandit problems,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.864107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 41585c88-d0b5-4d09-917b-abd1555c2f61 · outbound

This paper cites Feature detection with automatic scale selection,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Feature detection with automatic scale selection,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.853017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3362e211-5df1-4e6c-835c-7915d0af27af · outbound

This paper cites Applicability of deep learning to dynamically identify the different organs of the pelvic ﬂoor in the midsa gittal plane,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Applicability of deep learning to dynamically identify the different organs of the pelvic ﬂoor in the midsa gittal plane,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.841160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 03e8b010-2600-4208-9145-b45099ae188f · outbound

This paper cites Algorithms for hyper-parameter optimization,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Algorithms for hyper-parameter optimization,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.829729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e2605299-e1c3-458d-ab7f-6be4759df116 · outbound

This paper cites Random search for hyper-par ameter opti- mization.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Random search for hyper-par ameter opti- mization

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:22:31.817637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ff606954-0b1d-4047-b104-348d537a38f8 · outbound

This paper cites Coordinate descent algorithms,.

BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation Coordinate descent algorithms,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T00:22:31.767360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.