{"as_of":"2026-08-13T06:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:91ff02386ecca53659efa70d9a2d3f9f649806a126cacc79b95759e79e601607","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T20:15:26.649416Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-22T15:32:15.293888Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-22T15:34:57.779679Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":"2501.09138","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2501.09138","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Few-shot adaptation of training-free foundation model for 3d medical image segmentation","venue":null,"work_id":"b93a4b94-ece0-47b0-a458-4981d75525f9","year":2025},"citing_paper":{"arxiv_id":"2505.06907","last_updated":"2026-05-18T08:23:00Z","snapshot_observed_at":"2026-08-13T00:16:41.097127Z","submitted_at":"2025-05-11T08:57:53Z","title":"A Survey on Foundation Models for Personalized Federated Intelligence","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-22T15:32:15.293888Z"},"links":{"cited_paper":"/paper/2501.09138","citing_paper":"/paper/2505.06907"},"observation_digest":"sha256:053d2f3d205054f6e7a68f9f5eadbf926399db632e2c393d8f2d57e38dfa11a0","observation_id":"13a47842-886c-4260-8e2d-f284746695cb","resolution":{"observed_at":"2026-05-22T15:34:57.782169Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.09138/citation-record","integrity":"/paper/2501.09138/integrity","json":"/paper/2501.09138/citation-record.json","paper":"/paper/2501.09138"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.406692Z","title":"Segment anything in medical images,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.406692Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:d3e8b0167145919123b64be78aade94d41296a47e8d2d1845f395828dea0d880","observation_id":"f445f971-43df-400f-b32c-0d6daecf1c9c","resolution":{"observed_at":"2026-08-10T20:15:26.406692Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.412254Z","title":"Medical image segmentation using deep learning: A survey,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.412254Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:644a43ed499317e6c8a4c490c97ecbc80405a6f0a8e03ed88f1ebdf4c4a0fd67","observation_id":"6d457990-397b-4fb1-9970-50deee1eb48d","resolution":{"observed_at":"2026-08-10T20:15:26.412254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.417425Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.417425Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:576365c9842fd3afe915540203c46ee3b7b94669ecf64a402ce5a96fa44c1765","observation_id":"63786cfa-48ef-4410-ba57-8bc930693e93","resolution":{"observed_at":"2026-08-10T20:15:26.417425Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.422749Z","title":"Segment anything,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.422749Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:c50c39188bc2ab351c0a223ea82b27e299a8eee59a90b9cfe0e1d97984654a49","observation_id":"450cc084-1ae4-4080-9010-d417eae11452","resolution":{"observed_at":"2026-08-10T20:15:26.422749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00714","last_updated":"2024-10-28T16:37:57Z","snapshot_observed_at":"2026-07-06T18:55:41.459417Z","submitted_at":"2024-08-01T17:00:08Z","title":"SAM 2: Segment Anything in Images and Videos","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00714","snapshot_observed_at":"2026-08-10T20:15:26.427974Z","title":"Sam 2: Segment anything in images and videos,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.427974Z"},"links":{"cited_paper":"/paper/2408.00714","citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:d2a6617a7eb49de551572b58de21823d54e5279d10d6a908d467cff52a60242c","observation_id":"8d4a83e2-9927-4eb4-a320-bd28a7d56097","resolution":{"observed_at":"2026-08-10T20:15:26.427974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.433519Z","title":"Segment anything model for medical image analysis: an experimental study,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.433519Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:4500870547bd9eb9f9124e1169734abc646cecd9f5e318178eda6c8857a1a7d4","observation_id":"d75d76f9-3a72-4797-9ad8-bb8cec3f0dab","resolution":{"observed_at":"2026-08-10T20:15:26.433519Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:27.361163Z","title":"Segment anything model for medical image segmentation: Current applications and future directions,","venue":null,"work_id":"886da9a3-6cc6-4cf9-8c3e-6ed6358682fe","year":2024},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.439393Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:ec8440be1d5f5bcfd068c848c80b6b494ea81a786150d57100ea271ff31af9a9","observation_id":"06f1fb29-17da-45f6-b00a-e04b0a87ff60","resolution":{"observed_at":"2026-08-10T20:15:27.366693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.15161","last_updated":"2024-09-14T05:30:41Z","snapshot_observed_at":"2026-08-13T05:42:52.575612Z","submitted_at":"2023-10-23T17:57:36Z","title":"SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.15161","snapshot_observed_at":"2026-08-10T20:15:26.444280Z","title":"Sam-med3d: Towards general-purpose segmentation models for volumetric medical images,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.444280Z"},"links":{"cited_paper":"/paper/2310.15161","citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:fbd6808ac45ffa44c94c0d345a16fba07c966594b77e7a5c1667ea859cd75438","observation_id":"d524ce52-a242-43cc-824c-39155fa712a0","resolution":{"observed_at":"2026-08-10T20:15:26.444280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-13T02:40:23.887636Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-10T20:15:26.449983Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.449983Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:62b26e6f5e3223c111f03c5fc5afce2d22f9ced443a403dc4cffaffd19da001b","observation_id":"7fc8ce1e-e28d-4bd6-a33d-4bf673088399","resolution":{"observed_at":"2026-08-10T20:15:26.449983Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.455359Z","title":"Emerging properties in self-supervised vision transformers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.455359Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:69abb978f138e77b08c1196d4a1d3a7357e7f4f25156613b34dbe1b8ccde7ef7","observation_id":"1d4a4005-ecfb-42b9-adac-c79c349a983c","resolution":{"observed_at":"2026-08-10T20:15:26.455359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-11T10:12:11.384939Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-10T20:15:26.460291Z","title":"Dinov2: Learning robust visual features without supervision,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.460291Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:a1c871645cd6c043a2b5fcd1672464d3f673c1be35898b89105971c6629550e9","observation_id":"40a759dd-261d-46ac-8c14-6a0f330676fa","resolution":{"observed_at":"2026-08-10T20:15:26.460291Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.465622Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.465622Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:c40b44f47cb34db4a151eea5b92742ba212f5cc4d266211f8a7c9e8f8ba48bb7","observation_id":"b6efe707-216e-4c25-95ab-5cc98bd30087","resolution":{"observed_at":"2026-08-10T20:15:26.465622Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.05814","last_updated":"2022-10-15T21:18:49Z","snapshot_observed_at":"2026-08-12T19:07:50.172804Z","submitted_at":"2021-12-10T20:15:03Z","title":"Deep ViT Features as Dense Visual Descriptors","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.05814","snapshot_observed_at":"2026-08-10T20:15:26.470848Z","title":"Deep vit features as dense visual descriptors,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.470848Z"},"links":{"cited_paper":"/paper/2112.05814","citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:fa3a3da3051062b33f57f06f5004297ec86c54cf33c42372e75588a3a0e13dd2","observation_id":"a4ab7d16-9948-4a40-947d-d398aa7a4468","resolution":{"observed_at":"2026-08-10T20:15:26.470848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.14279","last_updated":"2021-09-29T09:01:07Z","snapshot_observed_at":"2026-08-11T12:40:58.351242Z","submitted_at":"2021-09-29T09:01:07Z","title":"Localizing Objects with Self-Supervised Transformers and no Labels","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.14279","snapshot_observed_at":"2026-08-10T20:15:26.476486Z","title":"Localizing objects with self-supervised trans- formers and no labels,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.476486Z"},"links":{"cited_paper":"/paper/2109.14279","citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:e8e0db96f2b553adf5d73f5a62ad10ad73f7fa25ecf686ac9f75e4f2a3d22964","observation_id":"a1321857-e0d0-4eef-85de-c9ec8ae86640","resolution":{"observed_at":"2026-08-10T20:15:26.476486Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:27.322100Z","title":"Sclip: Rethinking self-attention for dense vision-language inference,","venue":null,"work_id":"ab4fe7ea-429f-45fb-b072-3653f1497a17","year":2025},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.481954Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:c2ebd736df483714e48efe0388cdd8d55c6564f3ae4f9614eba566759cc59d39","observation_id":"56b01f1c-5310-4082-936c-be7adea7d67e","resolution":{"observed_at":"2026-08-10T20:15:27.327076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:27.305780Z","title":"Probabilistic prompt learning for dense prediction,","venue":null,"work_id":"4ac6b57b-e74d-4446-aeb5-1c9679722130","year":2023},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.487354Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:010cd058f2ab93b8f530954dffe5ffbd36060c97097ebabbfd35bdfd70b8a925","observation_id":"ad2d4eef-881b-4f8c-b690-6d6a8ff989e1","resolution":{"observed_at":"2026-08-10T20:15:27.310877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:27.288715Z","title":"Language-driven visual consensus for zero-shot semantic segmenta- tion,","venue":null,"work_id":"6bcbdfd1-f1f8-4e4c-b890-46d43229fbb1","year":2024},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.491750Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:0508a475adbaffb436e8a733d3e26f0aedb1fe6a29785f6d30dfb8e6a4621497","observation_id":"ad296c59-df0e-4cca-bf14-a0cf62f41813","resolution":{"observed_at":"2026-08-10T20:15:27.293874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:27.271948Z","title":"Generative semantic segmenta- tion,","venue":null,"work_id":"123d4a37-1af6-458b-94a9-3e11cb7aa82a","year":2023},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.496413Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:b5bce25105395c77630fc1ba14d2c91ae43b15dab3564dac3dbaffa2aa149841","observation_id":"50232f90-e213-46d5-86a7-d2223ed2d5d9","resolution":{"observed_at":"2026-08-10T20:15:27.277555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:27.254033Z","title":"Diffuse attend and segment: Unsupervised zero-shot segmentation using stable diffusion,","venue":null,"work_id":"1dbecdfe-ae7f-4719-ab56-6a4cd79ae8f1","year":2024},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.500666Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:8e1c563fdc89e1acb7a754f92b38c09155db1a718acd432564a27eb4f0cf798b","observation_id":"ebf2669a-7a40-4303-8a8e-b42fc613197d","resolution":{"observed_at":"2026-08-10T20:15:27.260239Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.505611Z","title":"Masked-attention mask transformer for universal image segmentation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.505611Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:06e6a41abda0d864d9d2150c510240a29abb8569bcbc1006c50b830759a232b5","observation_id":"598ca9c7-9da3-4f95-aa1e-9f636373a1f9","resolution":{"observed_at":"2026-08-10T20:15:26.505611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.510547Z","title":"Oneformer: One transformer to rule universal image segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.510547Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:944b5f9f10ed8fa4ae67dcf22604e7f85966d236e255264658f9e55ff5f27df0","observation_id":"4364acc7-86b4-4221-9608-13511efb0c32","resolution":{"observed_at":"2026-08-10T20:15:26.510547Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.05396","last_updated":"2023-04-10T18:20:29Z","snapshot_observed_at":"2026-07-06T15:14:32.823996Z","submitted_at":"2023-04-10T18:20:29Z","title":"SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.05396","snapshot_observed_at":"2026-08-10T20:15:26.515219Z","title":"Sam. md: Zero-shot medical image segmentation capabilities of the segment anything model,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.515219Z"},"links":{"cited_paper":"/paper/2304.05396","citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:a4dd1be6462e6253e8dc63d88f2eb24ca5b1d99f9b7f09dc142c8eee7c008fc6","observation_id":"e2b9327e-58b6-44be-8568-236d09aa0c72","resolution":{"observed_at":"2026-08-10T20:15:26.515219Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:27.209290Z","title":"The segment anything foundation model achieves favorable brain tumor auto-segmentation accuracy in mri to support radiotherapy treatment planning,","venue":null,"work_id":"05609805-0a57-4c9a-9e21-7d82438b928d","year":2024},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.519807Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:11cf1bf2625f5ee703f9dcb63ed55731b68adbf19fd856e22167b872b610560d","observation_id":"12071307-05cf-44cd-9db1-54fd5b1d6d6d","resolution":{"observed_at":"2026-08-10T20:15:27.216146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.04155","last_updated":"2023-04-09T04:06:59Z","snapshot_observed_at":"2026-08-10T12:11:26.767196Z","submitted_at":"2023-04-09T04:06:59Z","title":"Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.04155","snapshot_observed_at":"2026-08-10T20:15:26.524002Z","title":"Segment anything model (sam) for digital pathology: Assess zero-shot segmenta- tion on whole slide imaging,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.524002Z"},"links":{"cited_paper":"/paper/2304.04155","citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:677c27a26783c259391378aff27c9d75d44e20c22c0c50562b0c44154744fe8e","observation_id":"cecda38d-0bb4-49e6-89ad-be9d0a2f6804","resolution":{"observed_at":"2026-08-10T20:15:26.524002Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:27.187227Z","title":"Sam meets robotic surgery: an empirical study on generalization, robustness and adaptation,","venue":null,"work_id":"f46ba27e-b60d-4678-95ce-eed866a3cc48","year":2023},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.530564Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:c08a046c93e1973ad0ecfbd435d95dbb67be1da8b4e31b1e7d6e4c9d871a8d3b","observation_id":"2d0b4d09-6430-4560-b3f5-0bb10ceee7a8","resolution":{"observed_at":"2026-08-10T20:15:27.193411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03678","last_updated":"2023-08-11T04:23:29Z","snapshot_observed_at":"2026-07-06T15:23:52.761222Z","submitted_at":"2023-05-05T16:48:45Z","title":"Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.03678","snapshot_observed_at":"2026-08-10T20:15:26.535357Z","title":"Towards segment anything model (sam) for med- ical image segmentation: a survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.535357Z"},"links":{"cited_paper":"/paper/2305.03678","citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:4d8eb8bd4ad0f1e38415a75990f4481ad95b7df7f39141f1ac0a055dcc665a9e","observation_id":"21049c7b-ff66-4ec9-98e9-e7b04c3a106b","resolution":{"observed_at":"2026-08-10T20:15:26.535357Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.13785","last_updated":"2023-10-17T12:24:24Z","snapshot_observed_at":"2026-08-11T08:24:41.442941Z","submitted_at":"2023-04-26T19:05:34Z","title":"Customized Segment Anything Model for Medical Image Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.13785","snapshot_observed_at":"2026-08-10T20:15:26.540799Z","title":"Customized segment anything model for medical image segmentation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.540799Z"},"links":{"cited_paper":"/paper/2304.13785","citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:25f7aceac5da59a0db71b11c90479e5d0b8115855ecd8a8d519ce8c939356e92","observation_id":"7bd5408c-1c1a-4d57-80e3-bc2e6b967b71","resolution":{"observed_at":"2026-08-10T20:15:26.540799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.14133","last_updated":"2023-08-27T15:21:25Z","snapshot_observed_at":"2026-08-09T18:15:39.963094Z","submitted_at":"2023-08-27T15:21:25Z","title":"Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars","version":1},"cited_work":{"arxiv_id":"2308.14133","doi":null,"metadata_source":"pith","pith_arxiv_id":"2308.14133","snapshot_observed_at":"2026-08-10T20:15:26.753396Z","title":"Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars","venue":"cs.CV","work_id":"39db770e-15f4-425c-9e3d-0e72808087d3","year":2023},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.547678Z"},"links":{"cited_paper":"/paper/2308.14133","citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:70be87b68f77657778f5a7edc21e3846649a4c7f514f3cc67536db66ebf71728","observation_id":"b881b192-8fde-460f-826b-c201ee0933ab","resolution":{"observed_at":"2026-08-10T20:15:26.761413Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.16184","last_updated":"2023-08-30T17:59:02Z","snapshot_observed_at":"2026-07-06T16:12:26.218372Z","submitted_at":"2023-08-30T17:59:02Z","title":"SAM-Med2D","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.16184","snapshot_observed_at":"2026-08-10T20:15:26.553066Z","title":"Sam-med2d,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.553066Z"},"links":{"cited_paper":"/paper/2308.16184","citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:7b197efa7b69edbe86d0ac045f3f486731fa9fd6322a08b6411efacc9908b761","observation_id":"9aeebf98-5c1d-4919-b40c-ac90651099ac","resolution":{"observed_at":"2026-08-10T20:15:26.553066Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:27.169144Z","title":"Hiera: A hierarchi- cal vision transformer without the bells-and-whistles,","venue":null,"work_id":"5664010d-545e-43c3-8e3c-7e7208b7571a","year":2023},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.558582Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:815f7039a2c825825cb56afb9d1b9afc4d3e21f2911ecc1b673504e8843e1b11","observation_id":"dbfc6fd7-dda7-4e2f-8877-e274180fd392","resolution":{"observed_at":"2026-08-10T20:15:27.174762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.564285Z","title":"Masked au- toencoders are scalable vision learners,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.564285Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:ba09085e82a7b8bdf0f6677c7c9fe7b89bfda40ff107a6b4c1b7dcceb8fdd077","observation_id":"65ea7cd2-3b1a-4a90-b293-d08a934462b1","resolution":{"observed_at":"2026-08-10T20:15:26.564285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:27.140027Z","title":"Modern information retrieval: A brief overview,","venue":null,"work_id":"87a4c280-0aa3-4d2e-9892-20e71a7908b9","year":2001},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.569571Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:354bfb9bb698f25135f9fe038aa6dcbfee02be0f4cb796eeb7ce578e057d5b43","observation_id":"c960fab6-fe98-4aef-a594-70c9d8749feb","resolution":{"observed_at":"2026-08-10T20:15:27.145230Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00874","last_updated":"2024-12-04T23:51:25Z","snapshot_observed_at":"2026-08-12T23:09:58.583278Z","submitted_at":"2024-08-01T18:49:45Z","title":"Medical SAM 2: Segment medical images as video via Segment Anything Model 2","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00874","snapshot_observed_at":"2026-08-10T20:15:26.575060Z","title":"Medical sam 2: Segment medical images as video via segment anything model 2,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.575060Z"},"links":{"cited_paper":"/paper/2408.00874","citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:ae3fba438d3ab02f5f14db74aa08f24c845bb9d85e11d6522516f670fb56870f","observation_id":"17818555-8a34-4a45-926c-ee39e1af584c","resolution":{"observed_at":"2026-08-10T20:15:26.575060Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.08691","last_updated":"2023-07-17T17:50:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-17T17:50:36Z","title":"FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.08691","snapshot_observed_at":"2026-08-10T20:15:26.580978Z","title":"Flashattention-2: Faster attention with better parallelism and work partitioning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.580978Z"},"links":{"cited_paper":"/paper/2307.08691","citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:e4220ccf7e7fec3c05ceeb0cdb400536ced54669b2d7c20527887d0abf7353c8","observation_id":"ee457c02-4e50-4fd7-80ce-31658b297eec","resolution":{"observed_at":"2026-08-10T20:15:26.580978Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.586654Z","title":"U-net: Convolutional networks for biomedical image segmentation,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.586654Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:002ad3de9cc8883bef101ed0399a378b82b281e8eb07d72593d496965f010b4f","observation_id":"fb385659-e059-4e87-8481-0ccb9b105440","resolution":{"observed_at":"2026-08-10T20:15:26.586654Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.593576Z","title":"Unetr: Transformers for 3d medical image segmentation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.593576Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:8253b787e20f6f291c8e06dc294ee78b0936ca84e75345a39affc7d5a450f978","observation_id":"bc6c1364-6d71-459e-9119-e1639c05250d","resolution":{"observed_at":"2026-08-10T20:15:26.593576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.600446Z","title":"Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.600446Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:f766d6fbe204ad6b274df3def35bb06ae8bb92ecdc32115d508bceea787dd1f2","observation_id":"5f4527fb-b64f-45d0-92ff-06cd18588750","resolution":{"observed_at":"2026-08-10T20:15:26.600446Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:27.079026Z","title":"Segmentation of knee images: a grand challenge,","venue":null,"work_id":"b9064ddf-4eea-4ce1-af4d-ab55d13a7383","year":2010},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.607061Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:c37717d8a571bb085c9ce6178f47d447482942ce31eb27337987749ce0882580","observation_id":"673bf7d5-1ff2-4932-aa46-64f98bafdcfa","resolution":{"observed_at":"2026-08-10T20:15:27.084754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.612580Z","title":"Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.612580Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:9941995a75c4f5b69c4844c49ad90b09432ef02f5cc253456badbdf67f2fcd91","observation_id":"512a0496-88ee-4e62-9717-f4f12989f8e0","resolution":{"observed_at":"2026-08-10T20:15:26.612580Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:27.049037Z","title":"Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,","venue":null,"work_id":"fd207327-f60b-49b4-b73a-35ae2fc72b98","year":2015},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.618452Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:c68337c980c1f16c94964eb5b5c4451d03729bcef9e2d1236ea987c1f2369d38","observation_id":"4fe0e78b-cd67-4d25-8961-b74534466833","resolution":{"observed_at":"2026-08-10T20:15:27.054761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.623506Z","title":"Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.623506Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:3637441e79514dd2f3a469200e1e6812fba15f668b9877b4170ab931cc31a595","observation_id":"27041fe9-b916-4416-896c-e4abaf313309","resolution":{"observed_at":"2026-08-10T20:15:26.623506Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:27.020203Z","title":"The medical segmentation decathlon,","venue":null,"work_id":"fb51b710-4e75-4a52-b04c-b7d42ba9f84e","year":2022},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.628435Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:70797ef3b18a54ea29c6398be7abbbd39227982bc0b4563166ff2228274bbf1e","observation_id":"4df0f7da-02c4-4ae5-84ed-f4daf2faf98a","resolution":{"observed_at":"2026-08-10T20:15:27.025356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:27.003222Z","title":"Mean squared error: Love it or leave it? a new look at signal fidelity measures,","venue":null,"work_id":"f004bb4b-cf7c-4e68-9b9b-d7f1c47297e8","year":2009},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.633170Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:799edf5b02a5de87980072dd484536c7912a35ffa972663b6bfb3f5a1e097289","observation_id":"e45e730c-9473-4b4e-8b6f-decd317bc33e","resolution":{"observed_at":"2026-08-10T20:15:27.008688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.987338Z","title":"Image matching by normalized cross- correlation,","venue":null,"work_id":"6eb81d6d-9937-4283-8491-ba04725acbc3","year":2006},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.638158Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:925d4a6080010240688df972227c70588c1fd1657ebbc2ef6a12bab38f8ff3e8","observation_id":"95c0edd9-595f-41ff-9d8e-af73692b0f29","resolution":{"observed_at":"2026-08-10T20:15:26.991988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.971575Z","title":null,"venue":null,"work_id":"b10ec95a-cd3a-4878-88e4-a232e8a1cbb0","year":2009},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.643410Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:ebb727cf837283a208db6bf09356f487cbc54d14c91251ac1272c6016cb33697","observation_id":"a2600db4-25b9-49c9-b744-1e0abab74893","resolution":{"observed_at":"2026-08-10T20:15:26.976245Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T20:15:26.649416Z","title":"Pearson correlation coefficient,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T20:15:26.649416Z"},"links":{"citing_paper":"/paper/2501.09138"},"observation_digest":"sha256:d4d5e6b089cb93a1b4df850e5780cc2c285cc760eaefabe824e0b7b94a884a2d","observation_id":"9c47613b-f359-4086-8206-baa4176b837f","resolution":{"observed_at":"2026-08-10T20:15:26.649416Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.09138","last_updated":"2025-01-15T20:44:21Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T09:28:26.934251Z","submitted_at":"2025-01-15T20:44:21Z","title":"Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":30,"verified_exact":1,"verified_fuzzy":15},"total_outbound_references":46},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2501.09138."}