{"as_of":"2026-08-13T03:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:88e55b44e92813decb56423a67eff32ad890a2346feba7a29ecf29ab0e524210","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T20:30:17.287903Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2502.09804/citation-record","integrity":"/paper/2502.09804/integrity","json":"/paper/2502.09804/citation-record.json","paper":"/paper/2502.09804"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T20:30:17.907517Z","title":"Cancer today,","venue":null,"work_id":"25786c84-0c9a-49d5-9d3b-079566a1b374","year":2024},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.132863Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:c4f5725ed2ce5ff9beede6ac0db55da0e3687adb72484e9aeec124860e5aa84a","observation_id":"a0798066-59ca-487c-b337-35c26cb914ab","resolution":{"observed_at":"2026-08-07T20:30:17.912209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.892485Z","title":"Leukemia,","venue":null,"work_id":"8b703ed8-ff5b-4cee-b667-b03a8b5d6646","year":2023},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.138539Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:33b126ea2f64d00a8a644129b0ce9bee9a6e14c61fa459ef8bdc36c052d1a943","observation_id":"aba48ead-523a-4506-9f54-c71779a75754","resolution":{"observed_at":"2026-08-07T20:30:17.897049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.04381","last_updated":"2019-03-21T19:44:34Z","snapshot_observed_at":"2026-08-10T07:51:53.985038Z","submitted_at":"2018-01-13T04:46:26Z","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.04381","snapshot_observed_at":"2026-08-07T20:30:17.143425Z","title":"Mobilenetv2: Inverted residuals and linear bottlenecks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.143425Z"},"links":{"cited_paper":"/paper/1801.04381","citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:8bf04f11740c01ead2bcee614189867c2c0852b3d0cfdf64cb5f71b446bb2c82","observation_id":"15af3275-70a0-48a1-9f35-a54156ebbe4e","resolution":{"observed_at":"2026-08-07T20:30:17.143425Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03762","last_updated":"2023-08-02T00:41:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-06-12T17:57:34Z","title":"Attention Is All You Need","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.03762","snapshot_observed_at":"2026-08-07T20:30:17.148680Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.148680Z"},"links":{"cited_paper":"/paper/1706.03762","citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:d63bc729fa09f8a0494cd72ae4ea1c787ed3e6649ed3769dad9ffc0288471745","observation_id":"2071860e-424e-437f-a0fc-e35febc16271","resolution":{"observed_at":"2026-08-07T20:30:17.148680Z","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":"2026-08-07T20:30:17.877075Z","title":"You only look once: Unified, real-time object detection,","venue":null,"work_id":"1fd7614d-3f9a-4294-b69d-063789efe425","year":2016},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.153787Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:21a4bd9cdbd370ac1d49d2afae5369abb0c7bb54c9c88a4e0421ba81d6ac0c81","observation_id":"70e15309-930f-426e-9d57-2628f35d2786","resolution":{"observed_at":"2026-08-07T20:30:17.881846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.861174Z","title":"A fast and efficient cnn model for b-all diagnosis and its subtypes classification using peripheral blood smear images,","venue":null,"work_id":"808a74ac-1c42-433a-9fa1-d36f41ccba1f","year":2021},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.158295Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:f6af0b830cbbf9682051a8d3587f88634823047a7be8d610cef39e3896a8370f","observation_id":"a4a59f2c-c894-4f9e-b45d-b2ee654c3b8f","resolution":{"observed_at":"2026-08-07T20:30:17.866529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.846298Z","title":"All challenge dataset of isbi 2019 (c-nmc 2019) (version 1),","venue":null,"work_id":"7c93bdda-8293-4673-82fc-8d630483f496","year":2019},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.163569Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:af867bb6c1d7e1c85fca95c9b92b7bbd9245700035b76eeb43fa15ab26f3169a","observation_id":"fb718104-04c8-4d5d-8961-a80f600588df","resolution":{"observed_at":"2026-08-07T20:30:17.851166Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.831329Z","title":"Yolov11 - key features,","venue":null,"work_id":"c63a2f43-ed17-4652-9229-dde61b3a7396","year":2024},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.167902Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:634576fde8a82df3989dc56cbed7544d17ef5e6c956643384ec00b19e090bb0b","observation_id":"05ae1652-3433-4bfc-b05b-d90e4dfe78e6","resolution":{"observed_at":"2026-08-07T20:30:17.835950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.798298Z","title":"Yolov8: A novel object detection algorithm with enhanced performance and robustness,","venue":null,"work_id":"faf73c7c-55e6-4979-adec-a3f93202a6b7","year":2024},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.176992Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:f50d8b83362cb7e85164411189ffe18c8e6e64f3d78b912d65df32cc6ec15561","observation_id":"66a718e4-ff11-4ed3-8cc9-98dedbbb3fc6","resolution":{"observed_at":"2026-08-07T20:30:17.803727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1512.03385","last_updated":"2015-12-10T19:51:55Z","snapshot_observed_at":"2026-07-06T04:39:28.429064Z","submitted_at":"2015-12-10T19:51:55Z","title":"Deep Residual Learning for Image Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.03385","snapshot_observed_at":"2026-08-07T20:30:17.181190Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.181190Z"},"links":{"cited_paper":"/paper/1512.03385","citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:0eabd0cde27a5d41f83c26c53fbd16ca318c82300152212e7d9ba971d29dd4cc","observation_id":"e674d67d-c36f-40f4-a896-a896e9189a53","resolution":{"observed_at":"2026-08-07T20:30:17.181190Z","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":"2026-08-07T20:30:17.781943Z","title":"Early diagnosis of acute lymphoblastic leukemia using yolov8 and yolov11 deep learning models,","venue":null,"work_id":"7ac383e0-19d0-47f7-b275-121b45a5e4d0","year":2024},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.186548Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:567334c2433ce40c6467e26090c5e8ef8be8d3b1f7bb02d6e0292bb44dd0a17a","observation_id":"deeeb9f8-4b99-4692-a832-856c9e0a35c4","resolution":{"observed_at":"2026-08-07T20:30:17.786950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.767820Z","title":"DL4ALL: Multi-Task Cross-Dataset Transfer Learning for Acute Lymphoblastic Leukemia Detection,","venue":null,"work_id":"33ed1bf6-67a1-46b3-b480-194a921291f2","year":2023},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.191288Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:664e87143c9f64bd51224292f78793ca6d89e5e3e05fa846e61c906875966a74","observation_id":"eb961aa7-d6a9-4622-8b9e-a0a2ae989407","resolution":{"observed_at":"2026-08-07T20:30:17.772328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-12T14:19:29.389332Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-07T20:30:17.195606Z","title":"Very deep convolutional networks for large-scale image recognition,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.195606Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:99c670fcc1ad9a7bec61c49511fb6d7bff1c0f9f4c2e679e210ec1f0988f8a59","observation_id":"7e38abeb-1ea0-42a1-94f8-90d93918da58","resolution":{"observed_at":"2026-08-07T20:30:17.195606Z","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":"2026-08-07T20:30:17.752195Z","title":"Imagenet classification with deep convolutional neural networks,","venue":null,"work_id":"2da63dba-8ebd-4de9-a27a-b320c293c788","year":null},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.200758Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:0785e3d71a210afb4d8f30af650241d4977f886c586604e074c68b45939ad958","observation_id":"230277ae-98b2-40eb-b3fd-0c108898ae82","resolution":{"observed_at":"2026-08-07T20:30:17.757363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.4842","last_updated":"2014-09-17T01:03:11Z","snapshot_observed_at":"2026-08-10T12:32:10.061920Z","submitted_at":"2014-09-17T01:03:11Z","title":"Going Deeper with Convolutions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.4842","snapshot_observed_at":"2026-08-07T20:30:17.209426Z","title":"Going deeper with convolutions,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.209426Z"},"links":{"cited_paper":"/paper/1409.4842","citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:ced94a3d97c47659214748b4cab60b64c14afe6d3b757cfd0ab3cfb39ea02012","observation_id":"8b0f96cb-3364-477f-8129-1d35680d2fa4","resolution":{"observed_at":"2026-08-07T20:30:17.209426Z","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":"2026-08-07T20:30:17.728244Z","title":"A review of deep transfer learning and recent advancements,","venue":null,"work_id":"9fe14f1a-0a73-405b-a78a-383ee670cb81","year":2023},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.214074Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:cd7d7ef3823605d945e3a5d98530db08df83af775ce3cc650b3e4a2ad28dfc30","observation_id":"3ca427ed-e142-427f-8b43-6b36aa59e426","resolution":{"observed_at":"2026-08-07T20:30:17.733128Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.714464Z","title":"Object detection in autonomous maritime vehicles: Comparison between yolo v8 and efficientdet,","venue":null,"work_id":"a747bdab-9e63-40bc-8a57-193914a6640a","year":2023},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.218763Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:8009987a26f9803a3a941fc751d5cc71314ba52d322f24dbea6973543283fdc9","observation_id":"29a16d7d-d00f-4c3d-a0cd-6170fb982de2","resolution":{"observed_at":"2026-08-07T20:30:17.719112Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.700844Z","title":"Inception-v4, inception-resnet and the impact of residual connections on learning,","venue":null,"work_id":"5063f025-a7ef-4331-86eb-1686889b6667","year":null},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.223377Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:97cf2b1987f897731c5e520ba7b4b477a2645583d677bed6e5ddaae308fa79a4","observation_id":"9aaaec3f-efe1-45a9-a968-b8ad03e00b78","resolution":{"observed_at":"2026-08-07T20:30:17.705522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.686002Z","title":"A mobile application based on efficient lightweight cnn model for classification of b-all cancer from non-cancerous cells: A design and implementation study,","venue":null,"work_id":"34ceac4a-cc21-43b9-b9b9-05e3c1e3c856","year":2023},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.232719Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:40eae6da5ec217ea666b27335fb0622802d4f25b385de3f2ad1aa3a1641e1009","observation_id":"17e88677-17f4-4089-a4cf-f96e71cb84a8","resolution":{"observed_at":"2026-08-07T20:30:17.690896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.672107Z","title":"A2m-leuk: attention-augmented algo- rithm for blood cancer detection in children,","venue":null,"work_id":"d1898e73-59e2-466d-a62a-fa2ff53e6f5b","year":2023},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.236997Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:70f0845859fe38aa516f63075a4656cf96d67dc3dfe202e37886869ab69779af","observation_id":"e0c74f9b-fe40-41a0-bfe0-ef0f15d3357b","resolution":{"observed_at":"2026-08-07T20:30:17.676542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.658182Z","title":"Detection of acute myeloid leukemia using deep learning models based systems,","venue":null,"work_id":"eeccc1e9-419c-44e0-92e7-52259732ccc1","year":2024},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.241286Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:f2f1c95c875284eed192f69740f2d6093864dbc4ecba0b3cdca2e58e755ab420","observation_id":"b7569622-c000-496d-ae2b-a928660d40fc","resolution":{"observed_at":"2026-08-07T20:30:17.662624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.643371Z","title":"Enhanc- ing acute lymphoblastic leukemia classification with a rapid and effective cnn model,","venue":null,"work_id":"73617392-c413-445e-a25c-a2aebff7078e","year":2024},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.245587Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:39f47c4bf64046b7f48ae1e6f02542a1888dd06ed23f4a497e9996bfefd23e63","observation_id":"5769a8f4-c814-4c6b-9b27-f8bf6da56fae","resolution":{"observed_at":"2026-08-07T20:30:17.647876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.628644Z","title":"A fuzzy based classifier for diagnosis of acute lymphoblastic leukemia using blood smear image processing,","venue":null,"work_id":"88651219-a0e0-46e4-b15d-db9cbc33149a","year":2017},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.249978Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:172d39721fe0c64b752338f86d61209ad01262957b72721ba29c94eb89637f32","observation_id":"a3a73aa3-8461-486a-9ddf-b84b169b6a49","resolution":{"observed_at":"2026-08-07T20:30:17.633655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.18568","last_updated":"2024-08-12T06:11:33Z","snapshot_observed_at":"2026-08-12T23:52:12.548088Z","submitted_at":"2024-06-02T13:25:44Z","title":"A Diagnostic Model for Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.18568","snapshot_observed_at":"2026-08-07T20:30:17.254112Z","title":"A diagnostic model for acute lymphoblastic leukemia using metaheuristics and deep learning methods,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.254112Z"},"links":{"cited_paper":"/paper/2406.18568","citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:4c72fd887772745a70d96ed0a4d2f90467af7c710d9c11cd961490603eccd1d0","observation_id":"b911b561-d31d-46e1-bd6f-69f90d55501d","resolution":{"observed_at":"2026-08-07T20:30:17.254112Z","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":"2026-08-07T20:30:17.612731Z","title":"Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks,","venue":null,"work_id":"c53a15b4-bfa4-4abb-aedc-6852389498cd","year":2020},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.258466Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:811b8abaf94c0b31a110b70218b64ce43242f1e27bc40dc7b1a407ebe08ff81a","observation_id":"0dcedc54-4fe4-4cee-bdf3-1a627128a57d","resolution":{"observed_at":"2026-08-07T20:30:17.618420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.596502Z","title":"Vcaps-net: Fine- tuned vgg16 with capsule network for acute lymphoblastic leukemia detection on a diverse dataset,","venue":null,"work_id":"00949b23-ad5c-48fa-8bc4-9c8537cbf6fb","year":2024},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.262641Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:5d5b07b5560d0f1abe9a4fa018593ba4656ef08ce19d60d67945321c242138b6","observation_id":"aa2a7d50-371c-409c-97b5-641c5a45e5c2","resolution":{"observed_at":"2026-08-07T20:30:17.601642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.582226Z","title":"A hybrid detection model for acute lymphocytic leukemia using support vector machine and particle swarm optimization (svm-pso),","venue":null,"work_id":"c33bd49b-8fda-4ab4-aeb7-5424cb143be2","year":2024},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.267239Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:e3af573cc4a5c982628c924c7d20d7fde8e58f2676081407ffbf81fe46ac4e79","observation_id":"1f4f93e4-2a98-4d5e-9db3-fddb48c7a6ef","resolution":{"observed_at":"2026-08-07T20:30:17.586791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.566810Z","title":"Automated detection and classification of leukemia on a subject-independent test dataset using deep transfer learning supported by grad-cam visualization,","venue":null,"work_id":"d7c72c4e-ec34-42cd-8a40-221d4938b373","year":2023},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.271610Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:bdda35f914afcf3c65e5ddb54da74f7f0871f9fe3415ff74eb1bdd0dcc08b4a5","observation_id":"d3f6a018-40d4-4b20-ab3f-adf3791042a2","resolution":{"observed_at":"2026-08-07T20:30:17.571812Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.551545Z","title":"Imagenet large scale visual recognition challenge,","venue":null,"work_id":"0d9d0c7a-57e5-47db-9eda-17ec8e97088d","year":2015},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.275676Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:b94ab9669fe1e125c95aeb28423bb193467e4e06099537068d084669ba43d1e4","observation_id":"5a4ab02b-a3f6-4cff-94d4-1c70cb6ab747","resolution":{"observed_at":"2026-08-07T20:30:17.556307Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-07T20:30:17.535992Z","title":"Leukemia diagnosis in blood slides using transfer learning in cnns and svm for classification,","venue":null,"work_id":"dbeac51e-5af2-43b5-b5a7-f88ff552668b","year":2018},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.279822Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:71b42b175b7f171716942fd41af5c07c2a4a2915eeef9154c1904d03e0ef872e","observation_id":"4c86a635-280f-4a53-a7bb-fe75257a4b29","resolution":{"observed_at":"2026-08-07T20:30:17.541072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"7868.26548","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T20:30:17.411711Z","title":"Caffe: Convolutional architecture for fast feature embedding,","venue":null,"work_id":"2ae50fbd-2e4b-475d-9ea1-f37fb5098dac","year":2014},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.283792Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:268c54e58d939d7a4ad7ceeab6a3efc6205de29201b93d3fd19f54c8ab5296a3","observation_id":"f61b5555-8709-480f-bb2e-d64fb27e7360","resolution":{"observed_at":"2026-08-07T20:30:17.421248Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1405.3531","last_updated":"2014-11-05T08:34:48Z","snapshot_observed_at":"2026-07-06T03:43:41.561753Z","submitted_at":"2014-05-14T15:19:22Z","title":"Return of the Devil in the Details: Delving Deep into Convolutional Nets","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1405.3531","snapshot_observed_at":"2026-08-07T20:30:17.287903Z","title":"Return of the devil in the details: Delving deep into convolutional nets,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.287903Z"},"links":{"cited_paper":"/paper/1405.3531","citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:bee6b066d15c4d38d572c1df978b67305094f6d92369ede39b557b04965e0d01","observation_id":"08349494-44fd-48ab-af0d-aa204748e271","resolution":{"observed_at":"2026-08-07T20:30:17.287903Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T20:30:17.205266Z","title":"Available: https://proceedings.neurips.cc/paper files/ paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.205266Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:fa2cdaa63a63d75279686ca7982b4ea32076b02238a0fc46c75b2fe85ed73eec","observation_id":"ff472620-3892-4c9e-99a0-2af9483403e4","resolution":{"observed_at":"2026-08-07T20:30:17.205266Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1602.07261","last_updated":"2016-08-23T16:42:29Z","snapshot_observed_at":"2026-07-06T04:47:06.608643Z","submitted_at":"2016-02-23T18:44:39Z","title":"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1602.07261","snapshot_observed_at":"2026-08-07T20:30:17.227959Z","title":"Available: https://arxiv.org/abs/1602.07261","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.227959Z"},"links":{"cited_paper":"/paper/1602.07261","citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:a38c0d062826f33c740dfe7cd91270524c35b65a57c94e1cc7e527f9385eb0c2","observation_id":"6428ca05-649f-470c-bcd5-a9398af571eb","resolution":{"observed_at":"2026-08-07T20:30:17.227959Z","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":"2026-08-07T20:30:17.814500Z","title":"Available: https://docs.ultralytics.com/models/yolo11/ #key-features","venue":null,"work_id":"c5c4b2b2-27b4-4010-b50f-f4c530031160","year":null},"citing_paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T20:30:17.172564Z"},"links":{"citing_paper":"/paper/2502.09804"},"observation_digest":"sha256:a810eaac3ced99b7b551c52a266d67a352417fee1455b63354f65eea15d7d422","observation_id":"a88d65f1-5547-4c92-8a91-cd6c3bc39fa3","resolution":{"observed_at":"2026-08-07T20:30:17.819503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.09804","last_updated":"2025-02-13T22:43:28Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-10T05:35:40.835108Z","submitted_at":"2025-02-13T22:43:28Z","title":"Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":9,"verified_exact":0,"verified_fuzzy":24},"total_outbound_references":35},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2502.09804."}