{"as_of":"2026-08-16T13:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0a5c18b64dac5cd833cd08a2bb9cd0a7a363b3ab1976dab871ce74e0422a16dc","coverage":[{"denominator":22,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T12:05:00.600581Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"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/1908.07704/citation-record","integrity":"/paper/1908.07704/integrity","json":"/paper/1908.07704/citation-record.json","paper":"/paper/1908.07704"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:05:00.962452Z","title":"Dermatologist- level classification of skin cancer with deep neural networks","venue":null,"work_id":"78960462-e066-4243-90f0-a65c38ffb4ee","year":2017},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.495935Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:e64a0bc2ac8b9845d92f5a407d32e3525a029f2bdfac1c1f33f227fe352bdad7","observation_id":"871b45fb-b71d-48ca-b3d3-e7ea14273502","resolution":{"observed_at":"2026-08-14T12:05:00.967469Z","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":"2026-08-14T12:05:00.947795Z","title":"Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs","venue":null,"work_id":"577d27d6-c62f-4b1b-bfa7-dccabfe6e989","year":2016},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.502138Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:1f9b508966120c4f28d47e4acf80f56a1c5c7e72f8e36c90401627e43935a834","observation_id":"bba8e921-d9d7-48ca-a450-a9faa0a76ffd","resolution":{"observed_at":"2026-08-14T12:05:00.952655Z","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":"2026-08-14T12:05:00.932377Z","title":null,"venue":null,"work_id":"58c97065-893d-4ce1-9ad9-2d6a79d02cff","year":2018},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.508854Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:8d387ca3912749d541b27bbf45d723d5572e5da2e1bf4dd42e5c1712d55148e1","observation_id":"f21359d3-a760-4025-b4e5-c12846197725","resolution":{"observed_at":"2026-08-14T12:05:00.937590Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-14T12:05:00.917108Z","title":"Computer-aided diagnosis of liver tumors on computed tomography images","venue":null,"work_id":"5048a9bc-0384-40fc-82b7-b4dc918a49cb","year":2017},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.514346Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:43923eb5ec61588473bd893210d27273207adc706e4bb2fe2db009e22656ac9b","observation_id":"23b19246-d3d6-4829-8500-7817bfe55c87","resolution":{"observed_at":"2026-08-14T12:05:00.922231Z","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":"2026-08-14T12:05:00.901330Z","title":"Pulmonary nodule detection in CT images with quantized convergence index filter","venue":null,"work_id":"7d333b93-0710-45f8-a256-7e1199438682","year":2006},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.520050Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:f43e275e66328d2654d98e50ca1dfc4ed7afcff37729f7051cec600ab9f1a560","observation_id":"85d28e4d-e9cd-40a9-874a-bc7cf3f67676","resolution":{"observed_at":"2026-08-14T12:05:00.906723Z","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":"2026-08-14T12:05:00.884501Z","title":"Computer-aided detection of exophytic renal lesions on non-contrast CT images","venue":null,"work_id":"6477ac34-f621-428e-aab9-347338c3ceac","year":2015},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.526079Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:342f0ada0e933093346a22d0464ba49b102fdfdbe7c4702093c6c3f0411e5549","observation_id":"f65e0e98-0d53-4a6d-835c-0415c7cf8050","resolution":{"observed_at":"2026-08-14T12:05:00.889882Z","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":"2026-08-14T12:05:00.868533Z","title":"Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system","venue":null,"work_id":"d9f7c65b-49fc-40ab-9b60-7b2f217d9665","year":2004},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.531747Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:dc8241e9e9afcbece7a7f5dc4695f28e0aa02b8886b2238536b055879abed4e1","observation_id":"d0f61b61-e240-4106-8ba5-3c715f709f08","resolution":{"observed_at":"2026-08-14T12:05:00.873920Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:05:00.536587Z","title":"Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.536587Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:442ac612377410a003e1d6a7f3a7da7c9b5f3c23d6c4c60313f8062a4f16f4d7","observation_id":"286ac155-82f6-4653-92f1-2ca5028f6e2d","resolution":{"observed_at":"2026-08-14T12:05:00.536587Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-14T12:05:00.853381Z","title":"Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance","venue":null,"work_id":"3fcf441c-2cf4-4df2-b457-313e9a10d77c","year":2004},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.541424Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:7aa20b8df632ada2375ce74d0a07038f6d5586e038fda9e03bf9d9b897fb337c","observation_id":"3823c2c4-3078-43b6-b29f-ddcd81cf3287","resolution":{"observed_at":"2026-08-14T12:05:00.858456Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:05:00.546086Z","title":"Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.546086Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:6b31f4a029430b04f3adc5f7b6ff468c0be16cde93929b952c94ccd01abd4206","observation_id":"c7c9d601-9aae-4992-afe8-45dce42f8929","resolution":{"observed_at":"2026-08-14T12:05:00.546086Z","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":"10.1007/s10278-019-00227-x","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:05:00.631798Z","title":"Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges","venue":null,"work_id":"39a06b4a-e29d-4ea5-ad51-80a4677a9a0e","year":2019},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.550750Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:8136cebddedeacc0a7508a5b4ce5e8e595520eeba51dcd53a86cdf4713a27256","observation_id":"5e346cc8-b50f-40b2-a2d7-1023fb58dffd","resolution":{"observed_at":"2026-08-14T12:05:00.638896Z","resolver_source":"doi","status":"verified_exact"},"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":"2026-08-14T12:05:00.838480Z","title":"A fully automated algorithm for the segmentation of lung fields on digital chest radiographic images","venue":null,"work_id":"4d36a9e6-d4f0-4d9d-b654-06cfccb51c11","year":1995},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.555203Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:299b549ecb471f5246af48813a97e8cbc6c410209b2a35107a237f3c5974303c","observation_id":"6def0789-6065-4f41-9652-e6851d7eb6d7","resolution":{"observed_at":"2026-08-14T12:05:00.843396Z","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":"2026-08-14T12:05:00.823179Z","title":"Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database","venue":null,"work_id":"9cc30cca-eb74-45e1-b859-5523412bb1b9","year":2006},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.559628Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:1c43114b236ac5808a35d8f60a9bad15311d08e736e82a09f6ffff6c38088f7c","observation_id":"ff757c4d-edfa-4ef5-926d-6afac34cf16d","resolution":{"observed_at":"2026-08-14T12:05:00.828274Z","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":"2026-08-14T12:05:00.806527Z","title":"Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration","venue":null,"work_id":"c69fe81a-04c4-445f-b448-736eeb6e6a61","year":2014},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.564419Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:56439c2391a472e3abe9d215be5c6867e7970133e5c07feaaa3b5a877aa967f0","observation_id":"0f344608-559f-47b7-82f0-49876662252e","resolution":{"observed_at":"2026-08-14T12:05:00.812262Z","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":"2026-08-14T12:05:00.789383Z","title":"U-net: convolutional networks for biomedical image segmentation","venue":null,"work_id":"1a577f6c-96cd-4787-8e6c-5e073bad3800","year":2015},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.568750Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:3d74536f04555bb4d6e4aebaf8b03c34bb8ac07e9d069af5eff5fa1f0828ac2d","observation_id":"8e9380d9-10ed-4223-9b24-1cfda9573aa1","resolution":{"observed_at":"2026-08-14T12:05:00.794749Z","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":"2026-08-14T12:05:00.772391Z","title":null,"venue":null,"work_id":"7510113e-92d2-4ef4-b4d6-70ec18aaf052","year":2000},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.573302Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:3b3e725b707c6636d45e0335e3a9453b19d1e9a4da7772abcdcb6b26357f8859","observation_id":"95805ec7-2408-49f2-af41-8edf047dd8ca","resolution":{"observed_at":"2026-08-14T12:05:00.777435Z","resolver_source":"raw_fallback","status":"unresolved"},"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":"2026-08-14T12:05:00.754966Z","title":"Scalable Bayesian Optimization Using Deep Neural Networks","venue":null,"work_id":"3f1e74e2-a5b2-4df7-b038-34d4b6a4c3f5","year":2015},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.577677Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:cf6993826193fc9605904c6a6c28e562d19b7d3bda2581fe03174060c7d8c6c7","observation_id":"6316f466-a443-403c-bdd7-64948da843c5","resolution":{"observed_at":"2026-08-14T12:05:00.760572Z","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":"2026-08-14T12:05:00.736530Z","title":"Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization","venue":null,"work_id":"484ebadb-2ac3-41e2-8174-70b579b390c7","year":2018},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.582270Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:66e689d7ed1c4992a79f11fc571fd0374a21a5698a98977b32266824a2b0ba32","observation_id":"e81ff41d-5517-4bbc-8fc2-cb5ea28d1e85","resolution":{"observed_at":"2026-08-14T12:05:00.742161Z","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":"2026-08-14T12:05:00.719428Z","title":"Two public chest X-ray datasets for computer-aided screening of pulmonary diseases","venue":null,"work_id":"aae9c8c9-e114-4781-9d48-e60983c7d1df","year":2014},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.586787Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:7805ff170effd5f8615c739a37d2c6f2d33f89200f37ebc229209230b97cb707","observation_id":"b80a5e6d-ee04-4671-9c8e-0ad26f409b60","resolution":{"observed_at":"2026-08-14T12:05:00.724737Z","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":"2026-08-14T12:05:00.702633Z","title":"ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases","venue":null,"work_id":"da0c19eb-b732-4892-bb7c-0ca4ba3fb1c0","year":2017},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.591262Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:bdf812e8dbb5be46a5bd60ccdbcfd36247a6b35be02264625fe5f5185e24f2c0","observation_id":"5efa758d-28b9-460d-8f3b-6858ac5fa020","resolution":{"observed_at":"2026-08-14T12:05:00.708393Z","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":"2026-08-14T12:05:00.687223Z","title":"https://github.com/imlab-uiip/lung-segmentation-2d (Last visited on 2019/06/29)","venue":null,"work_id":"bdb8e8da-6603-4c15-8f4c-016fc91fd25a","year":2019},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.595763Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:041c5579920ff2e118c6a38ac910c8bd0ad75c305206e8973b1fa6a19449b478","observation_id":"e99b0e27-368a-459f-afeb-6a52f6350391","resolution":{"observed_at":"2026-08-14T12:05:00.691959Z","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":"2026-08-14T12:05:00.671266Z","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","venue":null,"work_id":"c8af3e44-4f1e-46b6-8396-ae8ebc180f35","year":2015},"citing_paper":{"arxiv_id":"1908.07704","last_updated":"2019-08-21T04:05:14Z","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T12:05:00.600581Z"},"links":{"citing_paper":"/paper/1908.07704"},"observation_digest":"sha256:0a838d2923c4fc5b660cb1e973a943527a47802b51c7496cfc0282a90d2afba3","observation_id":"f9250643-5b2e-4845-b22b-c23bd8a5370f","resolution":{"observed_at":"2026-08-14T12:05:00.676152Z","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":"1908.07704","last_updated":"2019-08-21T04:05:14Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-16T04:38:05.469647Z","submitted_at":"2019-08-21T04:05:14Z","title":"Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learning"},"reference_resolution":{"displayed":22,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":1,"verified_fuzzy":17},"total_outbound_references":22},"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 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:1908.07704."}