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.

pith.paper-citation-record.v1
2411.15621 v1

Coverage vector

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

35 of 35 outbound references displayed

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External citation measurements

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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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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Unresolved cited work

Reference 12

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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

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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

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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

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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

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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

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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

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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

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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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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

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On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Unresolved cited work

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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

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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

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On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data In: CEUR WORK- SHOP PROCEEDINGS

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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

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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

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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)

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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)

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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

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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

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On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Cancers14(4) (2022)

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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

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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

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On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data Unresolved cited work

Reference 35

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Pith citing papers

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