Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T20:39:32.523097Z
Paper Citation Record · LEDGER
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation
As of 18 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2412.05605.
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 2026-08-18T06:34:40.430872+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
55 of 55 outbound references displayed
- verified exact0
- verified fuzzy47
- unresolved8
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 18e6c4d3-c54f-4271-89fa-e985efcec6a4 · outbound
This paper cites Clinical applications of artificial intelligence in medical imaging and image processing—a review,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Clinical applications of artificial intelligence in medical imaging and image processing—a review,
Reference 1
- Resolution
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- raw_fallback, observed 2026-08-11T20:39:32.889454Z
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 9244132e-f346-47c3-afe2-21de7e9f7900 · outbound
This paper cites Deep learning-enhanced image segmentation for medical diagnostics,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Deep learning-enhanced image segmentation for medical diagnostics,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.883562Z
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 570da125-cb3a-4ae9-869c-d1cd795ee582 · outbound
This paper cites Ai in diagnostic imaging: Revolutionising accuracy and efficiency,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Ai in diagnostic imaging: Revolutionising accuracy and efficiency,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.877412Z
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 83ddc8a6-6a65-457d-831f-d3f4ed2586af · outbound
This paper cites Segment anything,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Segment anything,
Reference 4
- Resolution
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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 be79e81c-2e09-4e20-b0ca-f35d060f9088 · outbound
This paper cites Segment everything everywhere all at once,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Segment everything everywhere all at once,
Reference 5
- Resolution
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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 776ca609-f553-47a6-85af-74f1725f3a51 · outbound
This paper cites Segment anything model for medical images?,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Segment anything model for medical images?,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.856368Z
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 154921c6-bf6c-4b90-83c9-21c1b3761cfa · outbound
This paper cites LoRA: Low-Rank Adaptation of Large Language Models.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation LoRA: Low-Rank Adaptation of Large Language Models
Reference 7
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation de9d3c5e-881e-4cd4-83a5-b0155338a2b6 · outbound
This paper cites Adapters: A unified library for parameter-efficient and modular transfer learning,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Adapters: A unified library for parameter-efficient and modular transfer learning,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.849806Z
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 85177ccb-da63-4727-9f7d-2f7d569ea3aa · outbound
This paper cites Med- tuning: A new parameter-efficient tuning framework for medical volumetric segmentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Med- tuning: A new parameter-efficient tuning framework for medical volumetric segmentation,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.843114Z
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 1d0d0337-4861-4e77-abea-6e7a7436af50 · outbound
This paper cites Customized segment anything model for medical image segmentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Customized segment anything model for medical image segmentation,
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T20:39:32.425474Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c6e95299-fdec-4b9c-b467-ab004227b148 · outbound
This paper cites Sam-med3d: Towards general-purpose segmentation models for volumetric medical images,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Sam-med3d: Towards general-purpose segmentation models for volumetric medical images,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.832448Z
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 cadba79c-1eb4-47e6-a28b-193097c6abfc · outbound
This paper cites 3dsam- adapter: Holistic adaptation of sam from 2d to 3d for promptable tumor segmentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation 3dsam- adapter: Holistic adaptation of sam from 2d to 3d for promptable tumor segmentation,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.819522Z
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 ff15c82c-7625-490e-b37b-d3f2ade19401 · outbound
This paper cites Masksam: Towards auto-prompt sam with mask classification for medical image segmentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Masksam: Towards auto-prompt sam with mask classification for medical image segmentation,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.813342Z
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 252714cd-8195-42f3-b1ae-879f97682b73 · outbound
This paper cites Autoprosam: Automated prompting sam for 3d multi-organ segmentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Autoprosam: Automated prompting sam for 3d multi-organ segmentation,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.807457Z
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 18640cbc-e17c-4374-a453-1eb8fce48381 · outbound
This paper cites Towards segment anything model (sam) for medical image segmentation: A survey,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Towards segment anything model (sam) for medical image segmentation: A survey,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.801605Z
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 990b8420-7514-4baa-8cbe-efc8696ea778 · outbound
This paper cites Segment anything in medical images,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Segment anything in medical images,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.795090Z
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 af8ff61d-2b1e-46a6-8d8d-6da5481f7271 · outbound
This paper cites Autosam: Adapting sam to medical images by overloading the prompt encoder,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Autosam: Adapting sam to medical images by overloading the prompt encoder,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.788417Z
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 ad377442-e957-40cf-a932-68f9fedbf78c · outbound
This paper cites Segment any cell: A sam-based auto-prompting fine- tuning framework for nuclei segmentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Segment any cell: A sam-based auto-prompting fine- tuning framework for nuclei segmentation,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.781769Z
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 605db371-9f99-47a2-9154-5243fa710736 · outbound
This paper cites Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey
Reference 20
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eb3d28a7-83a5-4b07-ad9b-ec521cc5afda · outbound
This paper cites Learning Transferable Visual Models From Natural Language Supervision.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Learning Transferable Visual Models From Natural Language Supervision
Reference 21
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9a703ba8-af43-4ffd-80e8-072317af3141 · outbound
This paper cites Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T20:39:32.454362Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d2e03977-4d9d-4d5f-bd7f-523f74a59359 · outbound
This paper cites Segment everything everywhere all at once,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Segment everything everywhere all at once,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.774861Z
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 120ef0ad-b5d0-47c5-b3de-9bc166f611bc · outbound
This paper cites Seggpt: Segmenting everything in context,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Seggpt: Segmenting everything in context,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.768692Z
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 f164aa2c-1024-4216-85ff-b70e99c2342b · outbound
This paper cites Dinov2: Learning robust visual features without supervision,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Dinov2: Learning robust visual features without supervision,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.762292Z
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 0b3e1fb1-2781-4460-8631-b4951ff0e10f · outbound
This paper cites Detect any shadow: Segment anything for video shadow detection,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Detect any shadow: Segment anything for video shadow detection,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.755763Z
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 a84f5cc2-5f76-4b96-8faa-b4bd59eda47a · outbound
This paper cites Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Segment anything model (sam) for digital pathology: Assess zero-shot segmentation on whole slide imaging,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.749410Z
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 9ef1baae-f8f9-4c9e-b3f2-93189ebe2095 · outbound
This paper cites Accuracy of segment- anything model (sam) in medical image segmentation tasks,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Accuracy of segment- anything model (sam) in medical image segmentation tasks,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.743169Z
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 68f9d605-b0c9-42cf-b437-110906d6055b · outbound
This paper cites When sam meets medical images: An investigation of segment anything model (sam) on multi-phase liver tumor segmentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation When sam meets medical images: An investigation of segment anything model (sam) on multi-phase liver tumor segmentation,
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.737264Z
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 4962f8b3-6c89-420d-a0cf-9c945834d673 · outbound
This paper cites Can sam segment polyps?,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Can sam segment polyps?,
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.731157Z
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 5e516650-dcbd-470f-8a3d-3cd0bd611df7 · outbound
This paper cites Medical sam adapter: Adapting segment anything model for medical image segmentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Medical sam adapter: Adapting segment anything model for medical image segmentation,
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.826348Z
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 016dde48-e4ec-48c6-a989-42de2508da19 · outbound
This paper cites Sam-med2d,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Sam-med2d,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.724931Z
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 ffb7a417-3b59-4dbd-a01e-2904339a978b · outbound
This paper cites Medlsam: Localize and segment anything model for 3d ct images,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Medlsam: Localize and segment anything model for 3d ct images,
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.717819Z
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 d9d752f0-3762-46d7-963f-8cbe695bea61 · outbound
This paper cites Sam3d: Segment anything in 3d scenes,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Sam3d: Segment anything in 3d scenes,
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.710553Z
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 ac50028e-41cf-443d-8836-e7eb93e44cd9 · outbound
This paper cites Ma-sam: Modality-agnostic sam adaptation for 3d medical image segmentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Ma-sam: Modality-agnostic sam adaptation for 3d medical image segmentation,
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.703536Z
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 b61d8938-5097-40ff-93e5-ca4c2f8c9c5d · outbound
This paper cites St-adapter: Parameter-efficient image-to-video transfer learning,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation St-adapter: Parameter-efficient image-to-video transfer learning,
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.696801Z
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 6aaff1a1-2bab-46b6-80bf-f2c0596360ec · outbound
This paper cites Conditional prompt learning for vision-language models,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Conditional prompt learning for vision-language models,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.689833Z
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 f396bc64-2d16-424a-8f5e-ebc3a29787d3 · outbound
This paper cites Visual prompt tuning,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Visual prompt tuning,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.683256Z
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 a8467ffd-5139-4e82-b04e-fa3082b3e18c · outbound
This paper cites Learning transferable visual models from natural language supervision,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Learning transferable visual models from natural language supervision,
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T20:39:32.487961Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation da9749e2-d0ac-4cd3-9613-c8976b982feb · outbound
This paper cites Scaling up visual and vision-language representation learning with noisy text supervision,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Scaling up visual and vision-language representation learning with noisy text supervision,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.673017Z
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 ec1a0450-5ab5-4207-a5a5-b8ed2cc48956 · outbound
This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T20:39:32.492221Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 287a08ef-dabb-4033-9a89-3536f954d1b3 · outbound
This paper cites Vision-language transformer and query generation for referring segmentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Vision-language transformer and query generation for referring segmentation,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.666537Z
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 daafab1b-e04e-4168-87ae-8586d120627f · outbound
This paper cites Refsam: Efficiently adapting segmenting anything model for referring video object segmentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Refsam: Efficiently adapting segmenting anything model for referring video object segmentation,
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.660381Z
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 b8370196-2327-4c77-bc3c-817d1c264330 · outbound
This paper cites The kits21 challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase ct,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation The kits21 challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase ct,
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.653754Z
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 e8bf66a1-25c7-462a-a237-c46d871962c3 · outbound
This paper cites The liver tumor segmentation benchmark (lits),.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation The liver tumor segmentation benchmark (lits),
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.647203Z
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 388fcdea-ff1a-4803-9a86-7c8f561bfd95 · outbound
This paper cites The medical segmentation decathlon,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation The medical segmentation decathlon,
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.640747Z
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 ec802eda-a343-4671-8ef7-16e0c28dab3f · outbound
This paper cites Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.633751Z
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 d625ce0f-783b-4f1e-a5ff-cae95ef43753 · outbound
This paper cites Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.626698Z
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 d0f4be21-647b-4ee9-ab77-a6155eb67add · outbound
This paper cites Self-supervised pre-training of swin transformers for 3d med- ical image analysis,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Self-supervised pre-training of swin transformers for 3d med- ical image analysis,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.619963Z
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 56d5e1c0-2066-480e-a040-d2b62a4ca18f · outbound
This paper cites Amos: A large-scale abdominal multi- organ benchmark for versatile medical image segmentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Amos: A large-scale abdominal multi- organ benchmark for versatile medical image segmentation,
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.613164Z
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 e383c89a-9418-4cb9-a2e4-83e14e4e84b3 · outbound
This paper cites nnu-net: Self-adapting framework for u-net-based medical im- age segmentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation nnu-net: Self-adapting framework for u-net-based medical im- age segmentation,
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.606345Z
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 90baf382-bb12-43f1-b4b5-d8182844b9f2 · outbound
This paper cites U-net: Convolutional networks for biomedical image segmentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation U-net: Convolutional networks for biomedical image segmentation,
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.599209Z
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 a11c1a5c-b63a-468b-867a-c31db86ad87a · outbound
This paper cites Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,
Reference 53
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T20:39:32.517144Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1348c130-a23c-4559-b4ce-93828ddbda9c · outbound
This paper cites nnformer: V olumetric medical image segmentation via a 3d transformer,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation nnformer: V olumetric medical image segmentation via a 3d transformer,
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.588507Z
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 a858e335-c808-4310-985d-86ed04c52ac0 · outbound
This paper cites Unetr++: Delving into efficient and accurate 3d medical image segmentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation Unetr++: Delving into efficient and accurate 3d medical image segmentation,
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.581616Z
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 558791e4-4d52-4b8a-b650-763d4990f8ce · outbound
This paper cites 3d ux-net: A large kernel volumetric convnet modernizing hierarchical transformer for medical image seg- mentation,.
RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation 3d ux-net: A large kernel volumetric convnet modernizing hierarchical transformer for medical image seg- mentation,
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T20:39:32.574907Z
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.
Pith citing papers
No inbound Pith citation observations are available.