Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T14:09:36.923667Z
Paper Citation Record · LEDGER
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data
As of 13 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2411.15621.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
35 of 35 outbound references displayed
- verified exact0
- verified fuzzy28
- unresolved7
- parse uncertain0
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation cab79ace-7b61-41f8-b9c4-aa8eaafcf4b9 · outbound
This paper cites Cytometry Part A 95(7), 769–781 (2019).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Cytometry Part A 95(7), 769–781 (2019)
Reference 1
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 60a4ae8c-e139-4ec6-878d-6349a44ca46d · outbound
This paper cites Nature Communications8(14825), 2041–1723 (2017).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Nature Communications8(14825), 2041–1723 (2017)
Reference 2
- Resolution
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Source-reported events for the cited work
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Observation 3455fb90-74f8-48b5-8e20-809b80e5081c · outbound
This paper cites Nature biotechnology37(1), 38–44 (2019) Local and global feature learning for FCM data 15.
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Nature biotechnology37(1), 38–44 (2019) Local and global feature learning for FCM data 15
Reference 3
- Resolution
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Source-reported events for the cited work
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Observation e97991f2-45d2-4961-adb1-44c7d3e89d61 · outbound
This paper cites Proceed- ings of the National Academy of Sciences111(26), E2770–E2777 (2014).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Proceed- ings of the National Academy of Sciences111(26), E2770–E2777 (2014)
Reference 4
- Resolution
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Source-reported events for the cited work
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Observation 0801415a-e08f-4a0f-835e-58a9550ad153 · outbound
This paper cites Hematol- ogy 2010, the American Society of Hematology Education Program Book2010(1), 7–12 (2010).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Hematol- ogy 2010, the American Society of Hematology Education Program Book2010(1), 7–12 (2010)
Reference 5
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 5508bf64-23a5-439b-a380-035d6ee13917 · outbound
This paper cites Cytometry Part A pp.
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Cytometry Part A pp
Reference 6
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 4cef8b5c-2d63-421c-928c-39ce1e8c9a3b · outbound
This paper cites Springer (2020).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Springer (2020)
Reference 7
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 42945760-7e84-4f06-8cea-8a47ff08cdad · outbound
This paper cites Cytometry Part B: Clinical Cytome- try: The Journal of the International Society for Analytical Cytology74(6), 331– 340 (2008).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Cytometry Part B: Clinical Cytome- try: The Journal of the International Society for Analytical Cytology74(6), 331– 340 (2008)
Reference 8
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 064517fb-4328-450f-83ff-ba9ce5fbeb34 · outbound
This paper cites IEEE transactions on pattern analysis and machine intelligence 43(12), 4338–4364 (2020).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data IEEE transactions on pattern analysis and machine intelligence 43(12), 4338–4364 (2020)
Reference 9
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation fa73ceb6-8963-498f-941c-25ea7f33aa61 · outbound
This paper cites Frontiers in immunology12, 787574 (2022).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Frontiers in immunology12, 787574 (2022)
Reference 10
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 57ac0935-064e-4dd1-9b7c-597e2c3889e8 · outbound
This paper cites Semi-Supervised Classification with Graph Convolutional Networks.
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Semi-Supervised Classification with Graph Convolutional Networks
Reference 11
- Resolution
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Source-reported events for the cited work
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Observation ccce0f9d-cade-40b4-912f-954d179436a5 · outbound
This paper cites an unresolved cited work.
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Unresolved cited work
Reference 12
- Resolution
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Source-reported events for the cited work
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Observation e3a16e4e-7e3a-48ad-9d3f-bfeca12db531 · outbound
This paper cites Bioinformatics (Oxford, England) 33 (01 2017).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Bioinformatics (Oxford, England) 33 (01 2017)
Reference 13
- Resolution
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Source-reported events for the cited work
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Observation 2ec9531c-0199-4094-8ab1-8b33a160c984 · outbound
This paper cites In: Interna- tional Conference on Machine Learning.
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data In: Interna- tional Conference on Machine Learning
Reference 14
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 9f09989e-c960-4ff5-9541-4d2fbfc691f1 · outbound
This paper cites Cell 162(1), 184—-197 (2015).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Cell 162(1), 184—-197 (2015)
Reference 15
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 42cd046a-6b09-404a-acb6-f1831cc68cfe · outbound
This paper cites Bioinformatics33(21), 3423–3430 (2017).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Bioinformatics33(21), 3423–3430 (2017)
Reference 16
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 80904bf6-134d-4699-ab32-f5ec39a63af6 · outbound
This paper cites In: 2018 24th International Conference on Pattern Recognition (ICPR).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data In: 2018 24th International Conference on Pattern Recognition (ICPR)
Reference 17
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation e6665038-bc5a-4b48-97d2-5cb5d8414d13 · outbound
This paper cites Current protocols in immunology 120(1), 5–1 (2018) 16 L.
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Current protocols in immunology 120(1), 5–1 (2018) 16 L
Reference 18
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 8db06c3d-d573-47d4-a6e2-6f6354ee1182 · outbound
This paper cites Oncotarget7(44), 71915–71921 (2016).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Oncotarget7(44), 71915–71921 (2016)
Reference 19
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 50278e67-b85b-41ae-848d-c7849c4d8da0 · outbound
This paper cites In: Proceedings of the IEEE conference on computer vision and pattern recognition.
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data In: Proceedings of the IEEE conference on computer vision and pattern recognition
Reference 20
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Observation e0b154d1-b6e8-444d-a351-b789ada7154a · outbound
This paper cites cosFormer: Rethinking Softmax in Attention.
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data cosFormer: Rethinking Softmax in Attention
Reference 21
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 43e12e81-536e-4fa3-bf8e-a02bad380112 · outbound
This paper cites an unresolved cited work.
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Unresolved cited work
Reference 22
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation befe55cb-d97b-4f13-b309-6825a34937a2 · outbound
This paper cites Cytometry Part A95(9), 966–975 (2019).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Cytometry Part A95(9), 966–975 (2019)
Reference 23
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 2827cc4e-84cc-423e-9d1d-7e48e5d7b26f · outbound
This paper cites a case study: Flow cytometry.
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data a case study: Flow cytometry
Reference 24
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 8766496c-fcb9-4ae7-bffb-15959046d332 · outbound
This paper cites In: CEUR WORK- SHOP PROCEEDINGS.
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data In: CEUR WORK- SHOP PROCEEDINGS
Reference 25
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation d531da7c-ebeb-46f0-b68c-7074f8b3423a · outbound
This paper cites European Journal of Cancer122, 61–71 (2019).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data European Journal of Cancer122, 61–71 (2019)
Reference 26
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation a898378c-c12a-46b1-bb77-b2ee33b8d64a · outbound
This paper cites In: Advances in neural information processing systems.
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data In: Advances in neural information processing systems
Reference 27
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 489dedb5-8a28-4101-9f72-bb8ac716292b · outbound
This paper cites In: International Conference on Learning Representations (2018).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data In: International Conference on Learning Representations (2018)
Reference 28
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Source-reported events for the cited work
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Observation 5f65b367-4111-47c9-b94d-7155a30c33fa · outbound
This paper cites Communica- tions Biology 2(183), 2399–3642 (2019).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Communica- tions Biology 2(183), 2399–3642 (2019)
Reference 29
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation d355870a-88e7-4f36-9898-498051244160 · outbound
This paper cites In: 2020 25th International Conference on Pattern Recognition (ICPR).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data In: 2020 25th International Conference on Pattern Recognition (ICPR)
Reference 30
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation fa0c6f84-0f0b-4f68-9d11-a5f5bfa7fdaa · outbound
This paper cites In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision.
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision
Reference 31
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 5bc4a7f9-75c7-4ddd-9e1a-7913decae029 · outbound
This paper cites Cancers14(4) (2022).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Cancers14(4) (2022)
Reference 32
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation dfc7445c-35a8-4871-9199-c0e87f11024f · outbound
This paper cites Computers in Biology and Medicine p.
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Computers in Biology and Medicine p
Reference 33
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 0574d10c-2a62-400d-9e83-69b73007eae4 · outbound
This paper cites Advances in Neural Information Processing Systems34, 13266–13279 (2021).
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Advances in Neural Information Processing Systems34, 13266–13279 (2021)
Reference 34
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 08a0f8ab-afb9-48b6-bdd0-a793165ed5d2 · outbound
This paper cites an unresolved cited work.
On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Unresolved cited work
Reference 35
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.