{"as_of":"2026-08-16T09:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2e92e62d76a5192f650346ab8778ce3b6f6e52e91432cd0198f62b8d13e4936b","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T13:27:10.611348Z","state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/1908.05104/citation-record","integrity":"/paper/1908.05104/integrity","json":"/paper/1908.05104/citation-record.json","paper":"/paper/1908.05104"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T13:27:11.689385Z","title":"Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008[J]","venue":null,"work_id":"0f541665-1c4e-4670-bd09-131c6846dda4","year":2008},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.371223Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:08656d91ab287ce442c61b6ad594f02c45ccaa787d1d927a1216407dc13bb4a1","observation_id":"6c7f1dfe-7e00-4d4c-86c0-4dc88df97849","resolution":{"observed_at":"2026-08-14T13:27:11.702857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.660543Z","title":"Liver tumor volume estimation by semi-automatic segmentation method[C]","venue":null,"work_id":"4880ac8d-fb91-4fa6-99f3-e045c04221aa","year":2005},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.377890Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:3cd952bff8a907212c0b84207c6a9ae649f80f9cb09d727fdafe85ce76d06c6e","observation_id":"5ed83b6a-39ac-43e9-95c2-1f4db4eb4aae","resolution":{"observed_at":"2026-08-14T13:27:11.667864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.631462Z","title":"Acute ischemic stroke[J]","venue":null,"work_id":"0321d2c0-4975-4270-b0b9-a9f9a54899c8","year":2007},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.383928Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:aaf7431d0c32352fd02aa32135b724632cb0c4bc9179d7b99e480b3ab7dca15d","observation_id":"7d41b115-f1c7-4bc7-bc51-eea92870fafa","resolution":{"observed_at":"2026-08-14T13:27:11.645691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.600350Z","title":"Cost of stroke in the United King- dom[J]","venue":null,"work_id":"43aeb871-9551-458a-8ec8-18baca352c89","year":2009},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.389582Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:35cd6b73d4540fab78a997c30aa5f8a07615c45dc630632f3e325553eac23547","observation_id":"56160aab-7ef1-41ad-93f5-7ef997a2830c","resolution":{"observed_at":"2026-08-14T13:27:11.608788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.576303Z","title":"Interrater agreement for final infarct MRI lesion delineation[J]","venue":null,"work_id":"5752a4d0-f8fe-4707-87cf-4e9c2b9343c1","year":2009},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.394988Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:c671788c9d2cf88bbcd01d8d7fb5bfa597dc1ff7343635bb23f3c8a6a6aae380","observation_id":"5d483924-792d-4730-8a82-9e6aa038a646","resolution":{"observed_at":"2026-08-14T13:27:11.586279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.554615Z","title":"Measurement of in- farct volume in stroke patients using adaptive segmentation of diffusion weighted MR images[C]","venue":null,"work_id":"ce165323-14da-4320-bfc4-47b6b63fbc4d","year":1999},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.400243Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:b8cd7bf56a554cb103082144ad475a393870c4b5b70ccdf9e05f3ae12999fb73","observation_id":"9d0104f1-9f21-4c61-bee3-d85a44586c89","resolution":{"observed_at":"2026-08-14T13:27:11.561064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.537507Z","title":"A large, open source dataset of stroke anatomical brain images and manual lesion segmentations[J]","venue":null,"work_id":"2d1c059b-5c03-4796-b999-69fc7d21addb","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.406208Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:94a96577c1b2b923b6952a0f1956a1971bcca559dfaa72c4faf5b1b846c55228","observation_id":"5250beb9-d27d-41c3-883d-6b08cd44785b","resolution":{"observed_at":"2026-08-14T13:27:11.542942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.517578Z","title":"Ishemic Stroke Lesion Segmentation by Analyzing MRI Images Using Dilated and Transposed Convolutions in Con- volutional Neural Networks[C]","venue":null,"work_id":"aa5482a0-c581-4126-928a-f3fd1e303499","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.411776Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:30c2400f1106fbd8d48ef11cf7ecbadacaddaf27c86960925979e755c9f2f62b","observation_id":"b95c66ce-2a98-4767-994b-9cfe8b148d45","resolution":{"observed_at":"2026-08-14T13:27:11.525237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.491293Z","title":"Towards clinical diagnosis: Automated stroke lesion segmentation on multi-spectral MR image using con- volutional neural network[J]","venue":null,"work_id":"5695b0d8-2e24-4150-88a2-d59b5d290c98","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.417649Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:6a9a3a3a099f772ed7245ba1e5758e9620bf918f16b0e49bc163d01b68fc92ec","observation_id":"ce067bf7-1c28-4f2b-9eac-9fab43c30fd7","resolution":{"observed_at":"2026-08-14T13:27:11.500466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.468879Z","title":"Automatic segmentation of acute ischemic stroke from DWI using 3-D fully convolutional DenseNets[J]","venue":null,"work_id":"3746a2fc-fc4c-4d10-ae7a-0d1d25c200b6","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.426460Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:fc1985acf1eb4e7618902df832f58cfb5c308262c9d8cbf1618c74b4197da96d","observation_id":"af20b30b-5d5b-4842-a1a8-1f8c1b7e84b3","resolution":{"observed_at":"2026-08-14T13:27:11.476366Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.445510Z","title":"Stroke lesion detection using convolutional neural networks[C]","venue":null,"work_id":"0b7c8a79-8569-4405-901a-bca59abd1c05","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.432415Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:7b9abc268a7734d328fac7659c894b385de52035c92d7b541675f117ba5cbf6a","observation_id":"a23a4cf7-7b1e-435a-a22b-6350e82fa787","resolution":{"observed_at":"2026-08-14T13:27:11.453664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.420109Z","title":"Optimizing U- Net to Segment Left Ventricle from Magnetic Resonance Imag- ing[C]","venue":null,"work_id":"76571c47-6ea6-4782-a6c8-c71b01e9a8ee","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.440826Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:5227f2620a6f4e507528631cc0164d4272cb307a97dd2257669f977d42ff483b","observation_id":"bd0c848a-d8b2-43df-9c01-b553daff130c","resolution":{"observed_at":"2026-08-14T13:27:11.426397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.255991Z","title":"Classification of Atrial Fib- rillation with Pre-Trained Convolutional Neural Network Mod- els[C]","venue":null,"work_id":"6ab8d294-aca4-479d-8bb7-9573e63dc7a4","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.448643Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:42fcac9640168dff985eb1e1eb210d79781570c8856c3616eb991737fada6e56","observation_id":"4c1fefaa-664a-487c-8cc2-7b4e63ef0b86","resolution":{"observed_at":"2026-08-14T13:27:11.262452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.237871Z","title":"Drinet for medical image seg- mentation[J]","venue":null,"work_id":"abb6b98b-e5a4-4cd0-872f-b9922e8b0c62","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.455062Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:c92dd0d9812b9f2eeafee0cfc493c0f4db08f6753fee0920fc06b7740f688510","observation_id":"b1ce4616-8c49-451c-9333-350c493a0c88","resolution":{"observed_at":"2026-08-14T13:27:11.243665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.213007Z","title":"Automatic real-time CNN- based neonatal brain ventricles segmentation[C]","venue":null,"work_id":"5feb3d52-7b95-4a56-93d7-6a829f5ff250","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.460468Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:34b6755af2875c5ec77d25bc28c69a114b29b696927d6e90a682edf018307294","observation_id":"47809710-8d8b-46ef-9d7c-37ddefe5fe7c","resolution":{"observed_at":"2026-08-14T13:27:11.220632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.192308Z","title":"3D fully convolutional networks for co-segmentation of tumors on PET-CT images[C]","venue":null,"work_id":"23e251af-9ddb-4acb-9ddb-33d0af155e1c","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.465630Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:d8a08aaa4585b0a68731d6f369f92a7b9dd762c0b53c81ada8002ea8b547712a","observation_id":"da29bfb2-ef89-4e4d-ac65-daf8a6b46896","resolution":{"observed_at":"2026-08-14T13:27:11.199626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.166589Z","title":"Image segmentation of liver CT based on fully convolutional network[C]","venue":null,"work_id":"a85b095c-0c10-4d7e-823a-ca06209ed3b5","year":2017},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.470811Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:c37896f8d0d817460a4a9bffa6ef9e4162ba26327ed60fcc78a6a5d13a9dbb44","observation_id":"51feaefa-5c8f-4727-ac9f-2d3ee0f593b0","resolution":{"observed_at":"2026-08-14T13:27:11.177908Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.144840Z","title":"Querying Representative and Informative Super-pixels for Filament Segmentation in Bioim- ages[J]","venue":null,"work_id":"f4c373db-be21-4085-af7f-10418250ff03","year":2019},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.476585Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:527f0a82c123943a272d959d25a494a48f36bce8ebe0dfbd964b8bdb8aff75bf","observation_id":"d4fbdddc-28d6-49ad-b3fa-d0f25b645f3d","resolution":{"observed_at":"2026-08-14T13:27:11.151691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.125875Z","title":"Multivariate dy- namic prediction of ischemic infarction and tissue salvage as a function of time and degree of recanalization[J]","venue":null,"work_id":"f4d3b250-8798-4c0d-86a0-8f2ce621c5ce","year":2015},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.482026Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:899b7ea2687b8e849bf9c317196cef3473b9cbb1cdae9fd6382dcd81276df4ba","observation_id":"dc6ee409-77a8-4333-8b0e-18cb1e4e72c6","resolution":{"observed_at":"2026-08-14T13:27:11.133009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.105139Z","title":"Texture-based treatment prediction by automatic liver tumor segmentation on computed tomography","venue":null,"work_id":"75dc9b79-d4dd-4889-882d-debcf2509149","year":2017},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.487085Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:30b9dc15a026b1a2554989240663a083f73e80fde4ebb7bc815dc453eecb2aa3","observation_id":"b6cab1ae-9f26-45ca-a2f8-8c3fa2fba164","resolution":{"observed_at":"2026-08-14T13:27:11.111654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.082327Z","title":"An active learn- ing approach for stroke lesion segmentation on multimodal MRI data[J]","venue":null,"work_id":"4f088546-5820-4632-9de3-68ada381d7d1","year":2015},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.492502Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:80d4f82be9e88057a00dffa191304b268eb5e9ee22ad5ba0484938d2919a872f","observation_id":"a8e3c926-e736-450c-978c-76917c130b79","resolution":{"observed_at":"2026-08-14T13:27:11.090377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.059275Z","title":"An efficient automated methodol- ogy for detecting and segmenting the ischemic stroke in brain MRI images[J]","venue":null,"work_id":"32d649c5-d19f-486e-a799-1aae36d0494c","year":2017},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.497516Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:6d4f992811624f8ef51ab01c97819a1d1cb9da773e08f6ed0e484db175bd4aa5","observation_id":"f6fe058b-3a17-469d-931d-6a703d073d61","resolution":{"observed_at":"2026-08-14T13:27:11.068273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.036415Z","title":"Fully automatic acute ischemic le- sion segmentation in DWI using convolutional neural networks[J]","venue":null,"work_id":"b195704c-9d36-4b57-8a49-221a20fb7359","year":2017},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.502762Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:40dced98481ae59177e2bc020d91672068771f3cae47e7be1bde83a10db67b7a","observation_id":"b3a980e4-7050-42b5-abe9-4a131604551f","resolution":{"observed_at":"2026-08-14T13:27:11.042856Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:11.014184Z","title":"Automatic semantic segmentation of brain gliomas from MRI images using a deep cascaded neural network[J]","venue":null,"work_id":"be91a588-df63-4216-ba71-9f505710713d","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.508041Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:2b7d2e7bef7fd11bcd870015f978ec623f216040a413234be3d448360a587212","observation_id":"0f051b3e-2507-4d3d-8430-43838b620f07","resolution":{"observed_at":"2026-08-14T13:27:11.022284Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:10.989631Z","title":"Efficient multi- scale 3D CNN with fully connected CRF for accurate brain lesion segmentation[J]","venue":null,"work_id":"51ce80bf-f225-42c1-87da-7c27a77ea87d","year":2017},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.513075Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:4b30ac1c97589f1060c641dbf701d3fb801e8b5840d6f1e867c8b55af757cbd9","observation_id":"d9289efb-59cd-42e1-88e7-ad8bed7f612d","resolution":{"observed_at":"2026-08-14T13:27:10.996189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:10.965741Z","title":"A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection[J]","venue":null,"work_id":"c21e8d05-29fe-40a5-b04c-5f63be10a170","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.517818Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:5258dbfa8d4795c655202745bf0218507dbe4136ff7ff58a3f6a66661a8a73f9","observation_id":"3b9e37e9-4d91-46ec-80b5-bd5d3673c0c9","resolution":{"observed_at":"2026-08-14T13:27:10.975611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:10.938786Z","title":"Deep convolutional neu- ral networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning[J]","venue":null,"work_id":"6f6c5f61-8bd9-4907-a7d4-add7b2710162","year":2016},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.522925Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:e16f8873f5c3771399aba3fa7047ab1463b9d31f1a8f92646f7f7c414809827f","observation_id":"73f130d7-58e1-4260-9739-b39760487ca7","resolution":{"observed_at":"2026-08-14T13:27:10.945394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:10.917026Z","title":"3D convolutional neural networks for human action recognition[J]","venue":null,"work_id":"10b84c19-a72a-4997-a7b5-f424f19b9fdf","year":2012},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.528460Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:a797f91a38148b7e91f3fe1faa6538b611f350fd96014cf30f0723410c805774","observation_id":"79d77176-51a1-4dd0-b734-3cfbad620f69","resolution":{"observed_at":"2026-08-14T13:27:10.925155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:10.892040Z","title":"H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes[J]","venue":null,"work_id":"103bf54f-07b6-47c9-aad2-3ac5266e079a","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.535484Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:1f3f9c27bae998dffab139f81d8dc14e30c3b086b6f138b35273899d29b164ee","observation_id":"a0fc45d3-5d72-4f3c-afea-c39978b09204","resolution":{"observed_at":"2026-08-14T13:27:10.898120Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:10.868331Z","title":"Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy[J]","venue":null,"work_id":"1a1a0a55-379c-4ff2-ad5a-ee6e29df019f","year":1980},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.540722Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:76f3619176a31bc89499ff97da27efa650a7cd9b7986cf8730bfd3f451b7664a","observation_id":"1ff9bd1d-7103-465f-b9d0-b1de9a4318d8","resolution":{"observed_at":"2026-08-14T13:27:10.875862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:10.846888Z","title":"Focal loss for dense object detection[C]","venue":null,"work_id":"8a22d358-1228-4b8b-8367-614fe5eadace","year":2017},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.548458Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:0aab575e094992ca11e1a13ba049948264283caf6a8105740ea6015f76f12710","observation_id":"6b3ca3e6-4a9e-48bc-89f2-9b33013604f6","resolution":{"observed_at":"2026-08-14T13:27:10.852686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:10.826047Z","title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation[C]","venue":null,"work_id":"d9b401ba-c6ad-405a-a499-4ad9ef3ef7bf","year":2016},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.553597Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:64cb4f4d848c60d5bc068f4cac4aa1483dccfa22f2db380a950ff952e2ba96b0","observation_id":"8954ef0e-f7a1-4685-8054-1006acd4735d","resolution":{"observed_at":"2026-08-14T13:27:10.832934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:10.804516Z","title":"U-net: Convolutional networks for biomedical image segmentation[C]","venue":null,"work_id":"ebf2e7a2-19a6-4455-bebf-f3aa9ae9818e","year":2015},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.559234Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:bffded0e87838c20175b621e31a3d4a230d8a70236af0be20b9f4acf0ce8bcbd","observation_id":"59c5edf2-ce30-4b36-abcf-77b8d293ebf6","resolution":{"observed_at":"2026-08-14T13:27:10.811219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:10.784943Z","title":"Squeeze-and-excitation networks[C]","venue":null,"work_id":"483e1eb7-a433-48b2-a804-3e0d9e035feb","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.565433Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:52a68ff04f56ea6e567f548c07e0f4d44299258428b5906863d71b3426dd14d2","observation_id":"aed8d313-9210-4be0-b3f8-9f18fffaece6","resolution":{"observed_at":"2026-08-14T13:27:10.790346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:10.765732Z","title":"Delving deep into rectifiers: Sur- passing human-level performance on imagenet classification[C]","venue":null,"work_id":"756bbae0-dfba-4db9-9a0b-0daee29b3710","year":2015},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.570589Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:4ca4670c023d624625b2ddfbbadd00a35560f002845318db02cff5e66b2e5175","observation_id":"1565238f-6c97-45c7-8627-8a606861065e","resolution":{"observed_at":"2026-08-14T13:27:10.772043Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-14T13:27:10.577873Z","title":"Adam: A method for stochastic optimization[J]","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.577873Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:43041b9395591cdef2d66fc68efb877ae531d32fe6b6aba7684f76fda4978ab1","observation_id":"75495b2c-bcf5-40f0-bc29-9865e7f04030","resolution":{"observed_at":"2026-08-14T13:27:10.577873Z","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":"2026-08-14T13:27:10.748027Z","title":"A comparison of automated lesion segmentation approaches for chronic stroke T1-weighted MRI data[J]","venue":null,"work_id":"22625db1-13b5-4fd8-9dcf-c61714fedf5f","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.584997Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:935a4f6d5f5dec19be798720a0f2fbdc5b0f723c8884a9cdd8f9d64d3d433017","observation_id":"95215e7b-450c-4b08-b7ca-61911f64654f","resolution":{"observed_at":"2026-08-14T13:27:10.753387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:10.731033Z","title":"Segnet: A deep convo- lutional encoder-decoder architecture for image segmentation[J]","venue":null,"work_id":"7ce3ba14-0f7f-4bf7-a1f5-d2a6c3bb7572","year":2017},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.594858Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:81e9e5b19ff1000de330f54b4add0cf538bdccc0dc6493764717e3c2a98b98d0","observation_id":"b5e791bc-2dc5-4d7a-8a72-93f5cbfc3103","resolution":{"observed_at":"2026-08-14T13:27:10.736381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:10.712836Z","title":"Pyramid scene parsing network[C]","venue":null,"work_id":"2b9e257b-7518-4223-81e8-3204428575ed","year":2017},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.600422Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:9c43c7e657784e6708b770c2bbf2bde694e07ab6f133ba9539548f0c4947755b","observation_id":"77e4aa6b-fc80-4119-8b3c-30e368fb4b7d","resolution":{"observed_at":"2026-08-14T13:27:10.718771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:10.694843Z","title":"Encoder-decoder with atrous separable convolution for semantic image segmentation[C]","venue":null,"work_id":"f2fa5b22-0748-40db-9722-4af251f6d17b","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.605671Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:1e65765fbcf932a79912f1d28acbe5d2a982cebac03a25b788bce4178d87dfcc","observation_id":"b9843152-2287-4081-a75d-2676274871ab","resolution":{"observed_at":"2026-08-14T13:27:10.700952Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T13:27:10.674269Z","title":"Beyond the pixel- wise loss for topology-aware delineation[C]","venue":null,"work_id":"5e6555f7-8533-4490-993a-31dd1e70968f","year":2018},"citing_paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T13:27:10.611348Z"},"links":{"citing_paper":"/paper/1908.05104"},"observation_digest":"sha256:8f4549b0abde32d56396c150571844b99ed61c1657e6d1b44393c342103501ab","observation_id":"16ba774e-8714-41bd-809a-e112a7fe0cd3","resolution":{"observed_at":"2026-08-14T13:27:10.682069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.05104","last_updated":"2019-08-14T12:54:04Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-16T04:43:14.364537Z","submitted_at":"2019-08-14T12:54:04Z","title":"D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":0,"verified_fuzzy":40},"total_outbound_references":41},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:1908.05104."}