RefSAM3D: Adapting SAM with Cross-modal Reference for 3D Medical Image Segmentation | Paper Citation Record

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

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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

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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

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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: verified fuzzy; raw_fallback, observed 2026-08-11T20:39:32.870237Z

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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: verified fuzzy; raw_fallback, observed 2026-08-11T20:39:32.863497Z

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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; no resolver link, observed 2026-08-11T20:39:32.452003Z

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+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 2026-08-21T06:32:19.484+00:00.

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