{"as_of":"2026-08-20T17:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:75ca3aa194284bd87b0b450b1f348e18b125627297e318c3714f8898826eb100","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T06:04:48.307575Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2506.06054/citation-record","integrity":"/paper/2506.06054/integrity","json":"/paper/2506.06054/citation-record.json","paper":"/paper/2506.06054"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T06:04:48.542124Z","title":"Fetal mri: A force for prenatal imaging of birth defects,","venue":null,"work_id":"0e5ca9df-4375-490b-8844-807e97f1f053","year":2021},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.232645Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:3e9bea8c8e80ebe991fd6afb7dca87275842fcbad69d4b47b8379354f1d06f44","observation_id":"2b58b8a3-a8a4-4230-bbe6-ebf41b586032","resolution":{"observed_at":"2026-08-07T06:04:48.544826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.535057Z","title":"Prenatal diagnosis of congenital heart defects: echocardio- graphy,","venue":null,"work_id":"000fcec5-0637-4213-82cf-96e8ed0713f5","year":2021},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.235571Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:31633d59459e2103301dd8a7f55f534675a6799d64994be06298e5df6f536991","observation_id":"aac3e6a2-32e5-436b-95fd-69609fdbccc4","resolution":{"observed_at":"2026-08-07T06:04:48.538267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.526939Z","title":"Role of four-chamber heart ultrasound images in automatic assessment of fetal heart: A systematic understanding,","venue":null,"work_id":"9e8a363c-4edc-49f4-bb48-0fe1ca3b5723","year":2022},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.238041Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:23d9217a579c8ae767cecffb6e6d3f42925452467d0b198d1c8701efe5b8ae4e","observation_id":"df7d9aa4-f0be-470a-bebf-841f84a61ea4","resolution":{"observed_at":"2026-08-07T06:04:48.529619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.519638Z","title":"An in-depth interpretation of the guidelines for prenatal ul- trasound examination (2012) by the sonographers’ association of the chinese medical doctors’ association","venue":null,"work_id":"65669a88-e497-404a-9202-542c05cc752c","year":2012},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.240880Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:0faaf961d93773f8bef5e78c9236e526b7c32dcb8dc6d62c8ed435c8a91badc4","observation_id":"14596166-d803-40b5-9ad7-68e2defd8d60","resolution":{"observed_at":"2026-08-07T06:04:48.522510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.512767Z","title":"Aium practice guideline for the performance of obstetric ultrasound examinations,","venue":null,"work_id":"1a88510f-56d7-4a9a-874d-bbaf045c00de","year":2013},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.243575Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:3c0dbd220a3c7ab44924fea2d40b69abd9a3c8448709be165433b2961c974885","observation_id":"3fa26c30-8ffc-44e5-9c0b-180cafac944d","resolution":{"observed_at":"2026-08-07T06:04:48.515487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.505777Z","title":"Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan","venue":null,"work_id":"5d2e8d18-4a83-498f-b890-147e25b7b336","year":2011},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.246679Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:278cca8d51b15d321d875ee3ed9b9a5454ec6cdc408bc694ccaa4f2cfc13a7f1","observation_id":"93fea52b-11f6-44db-bf7b-ebad8e556813","resolution":{"observed_at":"2026-08-07T06:04:48.508506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.499058Z","title":"Deep learning in image classification using residual network (resnet) variants for detection of colorectal cancer,","venue":null,"work_id":"2c7625e8-edb1-485e-a381-4bf6d6b132df","year":2021},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.249229Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:feac5d1ce4b26f89a850865a4bc74ecba6766bacf26fb0e7d54dcce777d6e9c2","observation_id":"3eb35285-9d62-44a4-8d75-9975fc42993f","resolution":{"observed_at":"2026-08-07T06:04:48.501687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.492113Z","title":"Automatic classification of fetal heart rate based on convolutional neural network,","venue":null,"work_id":"decc9ae5-8245-4a8a-85c0-5799f703c7fc","year":2018},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.252308Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:7fd5605dcd65c890929ba611e6f538282c6624d406cc88ee9828a56fd4d00542","observation_id":"fc162712-994b-4e84-942e-dc54d3a2c76d","resolution":{"observed_at":"2026-08-07T06:04:48.494745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.484834Z","title":"Fetal cardiac cycle detection in multi-resource echocardiograms using hybrid classification framework,","venue":null,"work_id":"938a0b02-f4f1-43ff-9393-b0084278a28f","year":2021},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.254508Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:6d44bc09d98029d95eb74e38e60135d92ad3b49b9ce4a880d48d0115d15f5937","observation_id":"32560310-8a7b-4d87-a965-d588ca9888bb","resolution":{"observed_at":"2026-08-07T06:04:48.487796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.476466Z","title":"Deep endpoints focusing network under geometric constraints for end-to-end biometric measurement in fetal ultrasound images,","venue":null,"work_id":"b9fef1d3-d586-4596-81af-4b5d8c778ba4","year":2023},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.257010Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:8a9a0acae020eed12e25967fc701822cf6d685fd0114ed6735d0b81001333f4b","observation_id":"73cc5557-ce0a-4d68-b73b-34c07630ca35","resolution":{"observed_at":"2026-08-07T06:04:48.479970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.468186Z","title":"Mobileunet-fpn: A semantic segmentation model for fetal ultrasound four-chamber segmentation in edge computing environments,","venue":null,"work_id":"3206055f-c7fe-4095-95eb-e4f3234432cd","year":2022},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.259639Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:f4065c5ea70110c024d42cc2471f26ebb799ab727d75a4f3322db4cfc3480053","observation_id":"5417101e-4a22-40e8-80cf-4b9611d94ded","resolution":{"observed_at":"2026-08-07T06:04:48.471746Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.461102Z","title":"A yolox-based deep instance seg- mentation neural network for cardiac anatomical structures in fetal ultrasound images,","venue":null,"work_id":"b054a227-52fe-4f6c-a905-7c754bd2d4a4","year":2022},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.262370Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:ca14e08a9e88a483f92c2519477eb333a43ff676974e1a5298eb8089f8252ad4","observation_id":"81bf0ea0-0ab1-4bcd-8570-d824e110e20d","resolution":{"observed_at":"2026-08-07T06:04:48.463930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.452999Z","title":"Fetal cardiac ultrasound standard section detection model based on multitask learning and mixed attention mechanism,","venue":null,"work_id":"22dc0bb6-9af0-4960-a4b6-10196db7a4cd","year":2024},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.264915Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:5aa3795164df64883ab84acadf4002433077045aa333b62e4fb40f9f409b9542","observation_id":"2d5678a9-b585-4324-82fc-149ce9d1efae","resolution":{"observed_at":"2026-08-07T06:04:48.455842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.445517Z","title":"Automatic fetal ultrasound standard plane recognition based on deep learning and iiot,","venue":null,"work_id":"30d03297-115c-4b9f-b72f-f2e2babb522d","year":2021},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.267418Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:b94a22b3ff45da077487c8dea318f9dc9a6c851d00ead9e7826cb464f18a3261","observation_id":"e600780c-9f57-4442-915a-e8b5d5951818","resolution":{"observed_at":"2026-08-07T06:04:48.448327Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.438703Z","title":"An ultrasound standard plane detection model of fetal head based on multi-task learning and hybrid knowledge graph,","venue":null,"work_id":"92649ec0-bdd1-4c68-9df2-8343b15a3747","year":2022},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.270524Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:0473f5a1b6aa4d8e131aa6709e30ceb7b28dc18e93fa9ce8e5c353790948653b","observation_id":"b26c396c-a589-4407-a7e4-b3c84db21deb","resolution":{"observed_at":"2026-08-07T06:04:48.441361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.430117Z","title":"Hfsccd: a hybrid neural network for fetal standard cardiac cycle detection in ultrasound videos,","venue":null,"work_id":"240b2945-42fc-47c4-ae9e-5ca423be2b23","year":2024},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.272917Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:edb7aa2424132f8c62a08f5d7a8016de39fea4c93e37dbdff9a711fb0c8cf43e","observation_id":"bb5730b5-d365-4321-8f4d-0d0de22e26f6","resolution":{"observed_at":"2026-08-07T06:04:48.432876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.423085Z","title":"Unsupervised domain adaptation for anatomical structure detection in ultrasound images,","venue":null,"work_id":"a82c435b-9a38-498b-a3b1-3de9be435546","year":2024},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.275759Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:c727fc27bff1de5a9fddbd3d4ae351d76f6938b4bd712d059f41915f17fa3239","observation_id":"b9eb3394-af03-480b-ba3e-7e4702ebabd2","resolution":{"observed_at":"2026-08-07T06:04:48.425790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.415908Z","title":"Sleep staging by bidirectional long short-term memory convolution neural network,","venue":null,"work_id":"c85fbfa2-12fc-4917-a176-d17f1ff9449a","year":2020},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.278163Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:e252d6938e34f733178939b7996bfd6ee8ff342cb8699acde5daadd261b471e0","observation_id":"f6af9b18-64f2-4f36-a1d2-2370d9356407","resolution":{"observed_at":"2026-08-07T06:04:48.418662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.408631Z","title":"An adaptive meta-imitation learning-based recommendation environment simulator: A case study on ship-cargo matching,","venue":null,"work_id":"5efe0d92-280a-4ce3-8905-9b7370470668","year":2024},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.281721Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:eef28835648e7cdd4f0704db0789bb33d97eaf16cc00cab5bf8157138a118551","observation_id":"f6a15444-9807-4499-8fe5-c47d197de40f","resolution":{"observed_at":"2026-08-07T06:04:48.411376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.400803Z","title":"M3-uda: A new benchmark for unsupervised domain adaptive fetal cardiac structure detection,","venue":null,"work_id":"840b56b3-0dfb-4ae9-b1f4-488ed5c627da","year":2024},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.284094Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:b282faed81cda79460a733fa99ac51dc3ae085967f633b924ca7633f8f08fcc2","observation_id":"e0ec81c0-6e89-4b68-a704-fb125830aa1a","resolution":{"observed_at":"2026-08-07T06:04:48.403556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.392525Z","title":"Farn: fetal anatomy reasoning network for detection with global context semantic and local topology relationship,","venue":null,"work_id":"bc5ee814-79a3-49a9-ac90-4ce6ea365fcc","year":2024},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.287480Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:3f4831772082eb5965f4be4114d5df5b0c193b07993800f6917e2ec5f485a14a","observation_id":"2b1fc099-d1f8-47a5-ab3c-167426b4149c","resolution":{"observed_at":"2026-08-07T06:04:48.396348Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.385126Z","title":"Efficient deep reinforcement learning-enabled recommendation,","venue":null,"work_id":"445fe7da-b1e4-4873-82ae-2c29c76e1251","year":2022},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.289750Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:ab8c4799f2c8e205375cd8e0709876ac1f82f441683d72c23ac6a1e2df01ebaa","observation_id":"f6deea8a-a6cc-4503-90fc-6594b154e613","resolution":{"observed_at":"2026-08-07T06:04:48.387829Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.377146Z","title":"Transfsm: Fetal anatomy segmen- tation and biometric measurement in ultrasound images using a hybrid transformer,","venue":null,"work_id":"3faf3ac2-d8f7-491b-bf4b-c6261d310bbf","year":2023},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.292560Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:f72c7775e83978d74ca63b561bf9e8ff0b5396d2d6cf79aa5e41cb757645ce9f","observation_id":"c3cbd40a-492b-4ec6-b25c-3a7fe609e225","resolution":{"observed_at":"2026-08-07T06:04:48.380074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.369172Z","title":"Fetal cardiac structure detection using multi-task learning,","venue":null,"work_id":"d56a88f5-faeb-42e4-9a3e-e308681ff1e8","year":2024},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.295072Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:f8fe127fe7fa53c15428a8cca65be9e17b45e2a25f5b505904435d01ed46d4e2","observation_id":"7d422bee-1cab-43f5-93f0-758b98d62a4c","resolution":{"observed_at":"2026-08-07T06:04:48.371956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12736","last_updated":"2025-03-13T09:35:17Z","snapshot_observed_at":"2026-08-19T08:17:54.757432Z","submitted_at":"2024-01-23T13:13:45Z","title":"$ShiftwiseConv:$ Small Convolutional Kernel with Large Kernel Effect","version":2},"cited_work":{"arxiv_id":"2401.12736","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.12736","snapshot_observed_at":"2026-08-07T06:04:48.335364Z","title":"$ShiftwiseConv:$ Small Convolutional Kernel with Large Kernel Effect","venue":"cs.CV","work_id":"dcc61a70-da3c-47b3-a7d7-fddf793f4e86","year":2024},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.297524Z"},"links":{"cited_paper":"/paper/2401.12736","citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:64e62e55b2c4a70d995fcf8f82198a2ab16edd8f73203735fdffccf0fc218c91","observation_id":"43c3c447-9677-4db3-a064-2bd453aaadc2","resolution":{"observed_at":"2026-08-07T06:04:48.339919Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.361648Z","title":"Understanding adamw through proximal methods and scale-freeness,","venue":null,"work_id":"2c9891de-5941-4ab3-8211-4445af3f2c19","year":2022},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.300317Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:6bf4f88d11ae79ab54e4c2bb1eff81bc1ce950f79062638d44318bc23bf3a033","observation_id":"6144a8bf-1edf-47d4-ad91-ec6a933f846f","resolution":{"observed_at":"2026-08-07T06:04:48.364509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-07T06:04:48.352991Z","title":"Generalized cross entropy loss for training deep neural networks with noisy labels,","venue":null,"work_id":"87f48104-377e-45d4-94e7-ea1c3fc7b1aa","year":2018},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.302631Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:d9dcd7b184518d8218f12d9c2a3b1c768473bc341a9f99372badd359b19d9524","observation_id":"c7b573b2-7146-4fb9-8567-128a5016e782","resolution":{"observed_at":"2026-08-07T06:04:48.356121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.05561","last_updated":"2021-12-10T14:12:32Z","snapshot_observed_at":"2026-08-16T17:35:37.401656Z","submitted_at":"2021-12-10T14:12:32Z","title":"Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.05561","snapshot_observed_at":"2026-08-07T06:04:48.304865Z","title":"Global attention mechanism: Retain information to enhance channel-spatial interactions,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.304865Z"},"links":{"cited_paper":"/paper/2112.05561","citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:54fb41a79fe16351e57bd8b736f0eac7fce95e64bdaca336a3982c118f0575a5","observation_id":"b9b24f8b-9de1-4de0-a714-570c001d2d3e","resolution":{"observed_at":"2026-08-07T06:04:48.304865Z","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":"2026-08-07T06:04:48.345520Z","title":"Conference on computer vision and pattern recognition,","venue":null,"work_id":"96f2f60c-4532-43a7-80ff-e710927ecbc4","year":1997},"citing_paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:48.307575Z"},"links":{"citing_paper":"/paper/2506.06054"},"observation_digest":"sha256:771e6e5ba44e6ff14b1e304e06a83034ce43551fdc9a14d19f71e47e402a1462","observation_id":"1d6a36a8-bb4f-4089-8627-774c3ffce0df","resolution":{"observed_at":"2026-08-07T06:04:48.348290Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.06054","last_updated":"2025-06-06T13:00:17Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-14T18:12:56.854755Z","submitted_at":"2025-06-06T13:00:17Z","title":"FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":1,"verified_fuzzy":27},"total_outbound_references":29},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.06054."}