{"as_of":"2026-08-14T20:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:660cf0cd17183bf21d6a5a0b8bbbf84ec3952cc1971a022f1bbeb89a6ca75a57","coverage":[{"denominator":34,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:55:43.786652Z","state":"measured"},{"denominator":34,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":34,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.15562/citation-record","integrity":"/paper/2506.15562/integrity","json":"/paper/2506.15562/citation-record.json","paper":"/paper/2506.15562"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:55:44.673072Z","title":"Global cancer observatory: cancer today","venue":null,"work_id":"d99d0be7-8a0f-45e3-9c5a-bd5bc5fce4c1","year":2020},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.612894Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:6d23b15af732e9b310a8246e4a9cad4accc031e3cf020a64bfe3c8d341f4b114","observation_id":"6bfe6c09-7aee-4411-aa57-a82b0e97e24a","resolution":{"observed_at":"2026-08-06T23:55:44.678237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3174/ajnr.a2640","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"MR imaging of neoplastic central nervous system lesions: review and recom- mendations for current practice","venue":"American Journal of Neuroradiology","work_id":"56492094-18bb-4725-b8dc-605d549950bd","year":2012},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.629388Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:ba24611aef517423be1462f095ef0dd5c05dbf633a3d2b3deabba40e6652ebb0","observation_id":"d58e0742-eb64-4625-aee2-2819882da2f0","resolution":{"observed_at":"2026-08-06T23:55:43.883005Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1161/01.str.24.1.35","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Classification of subtype of acute ischemic stroke: Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment","venue":"Stroke","work_id":"4302d63e-7ce6-4112-9c04-626079d3b3ac","year":1993},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.633965Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:6d5427cf6c25233f93a7be22f995fca4caf663c48b510dc1aa95c2f81e8c49b4","observation_id":"b8801de6-d9c8-4557-8d0c-5109cc3bc6d8","resolution":{"observed_at":"2026-08-06T23:55:43.865716Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T23:55:43.639012Z","title":"Simultaneous Truth and Performance Level Estimation (STAPLE): an algorithm for the validation of image segmentation","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.639012Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:11570d202f6fc8302ab1ad850c08691787e2f7d9e61d81f54dffc699af845b8b","observation_id":"1f461fd5-7ddd-40cf-883f-16964e5ebffb","resolution":{"observed_at":"2026-08-06T23:55:43.639012Z","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":"2026-08-06T23:55:43.643712Z","title":"A survey on deep learning in medical image analysis","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.643712Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:2fd11069a60523aac1c5cb50e6f7a87f108d6507c06b0c51e29dbc96bfce1191","observation_id":"ed15b495-d836-48d5-b2df-337d752ce830","resolution":{"observed_at":"2026-08-06T23:55:43.643712Z","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":"2026-08-06T23:55:44.657249Z","title":"The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)","venue":null,"work_id":"18a61ff0-43ba-46ce-96c3-5c2e44c83864","year":2015},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.648572Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:c7c702d65dc90619daf329e4c85829998ff2aa2f613801b02a75d49f79316d20","observation_id":"03f8c894-be5b-430b-9634-d49975f80d83","resolution":{"observed_at":"2026-08-06T23:55:44.662455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T23:55:44.638746Z","title":"U-net: Convolutional networks for biomedical image segmentation","venue":null,"work_id":"4294026d-2d32-45fe-ac8d-ea6d49e1768f","year":2015},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.653579Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:d1be64004296ef03d817d2643033f5c0c92c7800374953ef03685bb3aedda224","observation_id":"7175b82b-41d0-4d8b-aaff-19ba3236098c","resolution":{"observed_at":"2026-08-06T23:55:44.643868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.17059","last_updated":"2025-08-25T15:16:46Z","snapshot_observed_at":"2026-08-12T22:54:42.422286Z","submitted_at":"2024-08-30T07:38:28Z","title":"A Survey of the Self Supervised Learning Mechanisms for Vision Transformers","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.17059","snapshot_observed_at":"2026-08-06T23:55:43.658160Z","title":"A survey of the self supervised learning mechanisms for vision trans- formers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.658160Z"},"links":{"cited_paper":"/paper/2408.17059","citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:05fbcf55d9fd3c4fa4221a20ea05aa4a774de41697c10c2c07f3100b2a7ee2fa","observation_id":"a14c89d8-cb5c-49fd-bbf6-bf4c15a93d64","resolution":{"observed_at":"2026-08-06T23:55:43.658160Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.00634","last_updated":"2023-12-19T03:49:48Z","snapshot_observed_at":"2026-08-13T05:12:23.739795Z","submitted_at":"2023-12-01T14:54:44Z","title":"A Recent Survey of Vision Transformers for Medical Image Segmentation","version":2},"cited_work":{"arxiv_id":"2312.00634","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.00634","snapshot_observed_at":"2026-08-06T23:55:44.244094Z","title":"A Recent Survey of Vision Transformers for Medical Image Segmentation","venue":"eess.IV","work_id":"1ffdb6c7-4c58-4e3b-93b8-b07dc9c45efe","year":2023},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.662674Z"},"links":{"cited_paper":"/paper/2312.00634","citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:78b32f16b677d4d5f7a4abf3c166ebebae3b565072b09e4e1e26f6d7decba1fd","observation_id":"afcd0927-6076-4aa1-b263-c260bd91e7ef","resolution":{"observed_at":"2026-08-06T23:55:44.249219Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T23:55:43.667065Z","title":"Domain Adaptation for Medical Image Analysis: A Survey","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.667065Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:fc96ea4a13dbf1673a04e88494a15f19fdd8e75855b572f3d3c315e2756c4f08","observation_id":"589b8085-e062-494c-90e4-ad3afec5e4ab","resolution":{"observed_at":"2026-08-06T23:55:43.667065Z","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":"2026-08-06T23:55:44.621251Z","title":"Official Journal of the European Union, L 119, 4 May 2016, pp","venue":null,"work_id":"6746edb5-f30d-4764-9dcb-cab9630a0cad","year":2016},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.671429Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:30e64cb87bf126a51d4d73f5623db14275c61a2a24a3f8ec6eef388540018850","observation_id":"9b231d36-da66-48de-aac7-0496f009e579","resolution":{"observed_at":"2026-08-06T23:55:44.626530Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.08396","last_updated":"2024-03-29T12:50:38Z","snapshot_observed_at":"2026-08-13T11:42:48.269272Z","submitted_at":"2023-05-15T07:23:54Z","title":"MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation","version":5},"cited_work":{"arxiv_id":"2305.08396","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.08396","snapshot_observed_at":"2026-08-06T23:55:44.135261Z","title":"MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation","venue":"eess.IV","work_id":"36eb164a-0e57-48fa-91fc-db22f15f1f3f","year":2023},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.675915Z"},"links":{"cited_paper":"/paper/2305.08396","citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:da982f5221b97b55ffe3a1898d5b22f90fce9cb37861741a90222fc2f4b75f93","observation_id":"ba99937e-8078-4896-a74d-cf39b8fd03ca","resolution":{"observed_at":"2026-08-06T23:55:44.140183Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T23:55:44.602445Z","title":"nnU-Net: A Self-Configuring Method for Deep Learning-Based Biomed- ical Image Segmentation","venue":null,"work_id":"451f2e75-6a22-4101-998d-5040a6f7549d","year":2021},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.680937Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:1f9731b071824ceb6a2a4cb40b12385e133dbe5e96db5dd3622cc4a7d244593b","observation_id":"211baade-e871-4615-9d90-c0d3f7f99e8b","resolution":{"observed_at":"2026-08-06T23:55:44.608980Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T23:55:44.584452Z","title":"UNETR: Transformers for 3D Medical Image Segmentation","venue":null,"work_id":"8617273f-3cd8-4e1b-b7f6-415ace52751f","year":2022},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.685725Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:f11c1eaa48ae5adc01094da6de7bcdb17d42e7a515ac4f7e64bf67739d0efbc6","observation_id":"3f5f62ef-00ef-4986-9a34-3db809eb1b5e","resolution":{"observed_at":"2026-08-06T23:55:44.589623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.04056","last_updated":"2022-11-25T09:24:35Z","snapshot_observed_at":"2026-08-14T17:31:38.744041Z","submitted_at":"2019-01-13T20:38:16Z","title":"The Liver Tumor Segmentation Benchmark (LiTS)","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.04056","snapshot_observed_at":"2026-08-06T23:55:43.692688Z","title":"The Liver Tumor Segmentation Benchmark (LiTS)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.692688Z"},"links":{"cited_paper":"/paper/1901.04056","citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:bf3d6466b4f3457c8e354fe5735c00180ed69ab4ca7c45ff619857b5904c2d86","observation_id":"1455f798-8781-416e-bb92-20ab2f1d837b","resolution":{"observed_at":"2026-08-06T23:55:43.692688Z","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":"2026-08-06T23:55:43.698036Z","title":"H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.698036Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:af44742e23672300b15aa8aaf185a54c3faa1f1cd2a75642e868c7d583d123a1","observation_id":"b3e2eed7-a46b-47d0-9217-8ea7a5dcd324","resolution":{"observed_at":"2026-08-06T23:55:43.698036Z","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":"2026-08-06T23:55:44.567514Z","title":"pydicom: An Open Source DICOM Library","venue":null,"work_id":"2a47f604-6bc1-4689-b034-d521b53b9a0c","year":2025},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.702858Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:b6ec002f37f36ddf2af4dd9af595088f60b0e388e6a1dc8d6a4ab1e1266185de","observation_id":"7fe698da-88a6-4ce9-80c2-4543c4b8bb5b","resolution":{"observed_at":"2026-08-06T23:55:44.572468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T23:55:44.548283Z","title":"A Survey on Image Data Augmentation for Deep Learning","venue":null,"work_id":"80a72524-5dd3-49a9-858c-099350212903","year":2019},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.708755Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:b4ac99af9cb837957d80474b12295bf8229388f24a073974676c3f5cd3c75133","observation_id":"639f11ba-ced3-48bf-8242-6e59a7ec5f31","resolution":{"observed_at":"2026-08-06T23:55:44.554013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T23:55:44.530954Z","title":"Attention is all you need","venue":null,"work_id":"4864313e-5371-4cb9-82fb-2ef6a891da76","year":2017},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.713703Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:88b29ad282669727563b9fc69482e395fb49a530f2f3fb9cf9ae902d02bbfd62","observation_id":"fb2a6360-1bb0-45e4-ae85-faba8fdac770","resolution":{"observed_at":"2026-08-06T23:55:44.536078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2019.29264","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:55:44.018288Z","title":"A Stacked Multi-Connection Simple Reducing Net for Brain Tumor Segmen- tation","venue":null,"work_id":"76c3f624-267a-401c-ab99-b893914c58c9","year":2019},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.718456Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:37024a5125bc01396588d990d7e7baef0c990bc12b650c15a9d8b8c5785513d1","observation_id":"17cef672-b67b-4a5b-8d77-283ba7238466","resolution":{"observed_at":"2026-08-06T23:55:44.026631Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04306","last_updated":"2021-02-08T16:10:50Z","snapshot_observed_at":"2026-08-14T08:44:08.583921Z","submitted_at":"2021-02-08T16:10:50Z","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04306","snapshot_observed_at":"2026-08-06T23:55:43.723327Z","title":"Transunet: Transformers make strong encoders for medical image seg- mentation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.723327Z"},"links":{"cited_paper":"/paper/2102.04306","citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:3e846b408b1bd586d323406f6a981fd1313e0cf9b884568aaa94e9e58980b954","observation_id":"9bcb8eb8-6094-4b8b-a6c2-7b1606e59055","resolution":{"observed_at":"2026-08-06T23:55:43.723327Z","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":"2026-08-13T14:19:26.598265Z","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":"2026-08-06T23:55:43.728139Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.728139Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:f20e589121a02d9fff0f08534c805c5e18fecc27dccdd7f7a89997f3c6fd5887","observation_id":"550f7d84-bb6b-43c0-a45f-c7df351f6c03","resolution":{"observed_at":"2026-08-06T23:55:43.728139Z","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":"2026-08-06T23:55:44.513898Z","title":"HUT: Hybrid UNet transformer for brain lesion and tumour segmentation","venue":null,"work_id":"db612434-2ff5-4ffd-b379-d72e8eef4ffa","year":2023},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.732921Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:10b8ed809acd10350d44eb60a9bf6682bd25f845064ea47a2a8d179705f4e32a","observation_id":"6ea2ac31-e39b-4fd1-8aab-14f1f61a7c04","resolution":{"observed_at":"2026-08-06T23:55:44.519593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T23:55:43.737698Z","title":"HTTU-Net: Hybrid Two Track U-Net for Automatic Brain Tumor Segmentation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.737698Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:6774f067c39f50a7243ece809126cfa13ca0f34ab70d671c2ab48a4698e38a11","observation_id":"2b6ab794-8340-4044-8c4d-912933f33169","resolution":{"observed_at":"2026-08-06T23:55:43.737698Z","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":"2026-08-06T23:55:43.742592Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.742592Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:ec7cb8d8870e0716d90a728c8fb69632753d3134b29d4f663044190b1c895524","observation_id":"8a80248c-0153-4fc8-bdc2-01eaa32f3137","resolution":{"observed_at":"2026-08-06T23:55:43.742592Z","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":"2026-08-06T23:55:44.485657Z","title":"Segmentation Models","venue":null,"work_id":"f002aa0c-7db7-4699-bc3e-e9b9856ee81e","year":2025},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.746871Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:fd4727c4c687e34a18e5b791849c279d520cd7b9aaae5cee525aacacd73a8284","observation_id":"9386b3a0-1d42-45d7-bd68-0b8f2583ca61","resolution":{"observed_at":"2026-08-06T23:55:44.490561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T23:55:44.467415Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":"ef92137e-106f-4703-ad52-facf8f3b9438","year":2009},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.751921Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:58efe8560bd54e6edd4b602b2f221603b863ab96ab3bb537892a4e999f4dfefc","observation_id":"8004736e-94a1-4939-aa3f-ba0db63fa4c7","resolution":{"observed_at":"2026-08-06T23:55:44.473376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T23:55:44.449118Z","title":"Squeeze-and-excitation networks","venue":null,"work_id":"c3d5583b-7b1c-44e5-ac6f-cfcd80148f16","year":2018},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.757419Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:79fa97e535d4a94d027b79e50bb0667cfa071de0dd0bb75269e954ec7d029997","observation_id":"fbf00f28-c111-4e87-b281-827b7e96e005","resolution":{"observed_at":"2026-08-06T23:55:44.455209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T23:55:44.426820Z","title":"Cbam: Convolutional block attention module","venue":null,"work_id":"5932bd57-6bbc-4c26-8f36-da8c2a5d389d","year":2018},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.762399Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:3ab68ee44ae7e6b7468dcd580318123521277acff797c7c52f0c7ae3f5cf9e54","observation_id":"af69f30e-a793-4d67-ad76-81d9d790c00f","resolution":{"observed_at":"2026-08-06T23:55:44.435701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T23:55:44.410782Z","title":"Efficient attention: Attention with linear complexities","venue":null,"work_id":"a0919313-8030-4e89-aa8c-a3903e9da5b1","year":2021},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.767024Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:209f0e41fa97c1c212d08514a6ccd7e20fc086a33589830dd60f952af84b6265","observation_id":"d587f6c4-bfb0-4f0e-899b-9659675426e2","resolution":{"observed_at":"2026-08-06T23:55:44.415593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T23:55:44.393642Z","title":"Aggregated residual transformations for deep neural networks","venue":null,"work_id":"9d5ca839-7398-4b72-b755-f7978d8581aa","year":2017},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.772455Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:136dab42fe5243d8bd82acf2fea626951b836a2c5c0790c97a03dd2e94f89aa2","observation_id":"f4fa2002-d26a-4a54-8c49-2443c8d3dd55","resolution":{"observed_at":"2026-08-06T23:55:44.398915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T23:55:43.777142Z","title":"V-Net: Fully Convolutional Neu- ral Networks for Volumetric Medical Image Segmentation","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.777142Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:c09680f8b5547b6fd4ab77c2b6ed61103fbe3b02013d4bec67eac1bc3ca99ce7","observation_id":"4439dcd1-3b22-418a-9e69-cf39efb6ddd9","resolution":{"observed_at":"2026-08-06T23:55:43.777142Z","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":"2026-08-06T23:55:44.375469Z","title":"Unet++: A nested u-net architecture for medical image segmentation","venue":null,"work_id":"a460b8d4-531c-4e7f-ad7b-faf558a0ed44","year":2018},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.781727Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:794cac17eefb3b130029587f23a03f02936a86c030a067e9e8451af13fd5cff1","observation_id":"d815eb99-9725-42a9-bb75-5151d302098f","resolution":{"observed_at":"2026-08-06T23:55:44.381436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+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":"2026-08-06T23:55:44.359056Z","title":"MM-BiFPN: multi-modality fu- sion network with Bi-FPN for MRI brain tumor segmentation","venue":null,"work_id":"86ceda1e-6906-475c-957b-08248d4ea982","year":2021},"citing_paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T23:55:43.786652Z"},"links":{"citing_paper":"/paper/2506.15562"},"observation_digest":"sha256:216f5bb8dedd1fb36a5e1b281207ccbec70a431a5c709ba4e22fac2ad010a602","observation_id":"1ae142fa-47b5-4711-a756-c0eae0d37d55","resolution":{"observed_at":"2026-08-06T23:55:44.364049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.15562","last_updated":"2025-07-30T09:53:31Z","latest_version":2,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-12T11:47:07.912916Z","submitted_at":"2025-06-18T15:36:37Z","title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention"},"reference_resolution":{"displayed":34,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":11,"verified_exact":4,"verified_fuzzy":18},"total_outbound_references":34},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2506.15562."}