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Paper Citation Record · LEDGER

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis

As of 19 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2506.20333.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.20333 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:56:08.467715Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cc417e06-e329-4a9d-8a73-f1fdd02f926c · outbound

This paper cites In: European conference on computer vision.

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis In: European conference on computer vision

Reference 1

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Observation 29090ad7-8f42-45c7-bd08-b9395e7db241 · outbound

This paper cites In: 2024 IEEE International Conference on Bioin- formatics and Biomedicine (BIBM).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis In: 2024 IEEE International Conference on Bioin- formatics and Biomedicine (BIBM)

Reference 2

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Observation c667e588-31c6-41d9-99c4-19dd2635d336 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 3

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Observation 698d567b-6b27-4f65-8419-492a6ca345ea · outbound

This paper cites Advances in parasitology 95, 315–493 (2017).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis Advances in parasitology 95, 315–493 (2017)

Reference 4

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Source-reported events for the cited work

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Observation e421ac74-6284-4adf-a502-b55047155a76 · outbound

This paper cites ISPRS Journal of Photogrammetry and Remote Sensing 162, 94–114 (2020).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis ISPRS Journal of Photogrammetry and Remote Sensing 162, 94–114 (2020)

Reference 5

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Source-reported events for the cited work

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Observation 67c2d0a9-827d-40fc-aba0-ceb3b752e95d · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 6

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Observation 04340cb3-75c8-442d-97ca-ea50493760f3 · outbound

This paper cites Proceedings of the IEEE 86(11), 2278–2324 (1998).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis Proceedings of the IEEE 86(11), 2278–2324 (1998)

Reference 7

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation b7775015-a327-4def-8d4b-21612c497fa1 · outbound

This paper cites SPMamba: State-space model is all you need in speech separation.

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis SPMamba: State-space model is all you need in speech separation

Reference 8

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Observation 17bc9682-2de2-46a7-981d-d8460a3f8e12 · outbound

This paper cites In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Reference 9

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Observation 99a99590-fc20-4c90-9a46-065b88e267e8 · outbound

This paper cites In: ICASSP 2025 (2025).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis In: ICASSP 2025 (2025)

Reference 10

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Observation c6b5d7d3-9bd0-4a5d-9694-1423264565e5 · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence (2024).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

Reference 11

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Source-reported events for the cited work

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Observation d5415ba8-f285-4513-8a50-e86f83f7fc50 · outbound

This paper cites An efficient encoder-decoder architecture with top-down attention for speech separation.

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis An efficient encoder-decoder architecture with top-down attention for speech separation

Reference 12

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Observation 93f2760e-dd11-4103-9762-77808b42faaa · outbound

This paper cites In: International Conference on Medical Image Computing and Computer- Assisted Intervention.

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis In: International Conference on Medical Image Computing and Computer- Assisted Intervention

Reference 13

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Observation 9b55cde1-ae40-4067-a491-b4bab6f9bcdb · outbound

This paper cites Advances in neural information processing systems 37, 103031–103063 (2024).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis Advances in neural information processing systems 37, 103031–103063 (2024)

Reference 14

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Observation 5f925a9e-ca76-499b-a387-3be07ef15c95 · outbound

This paper cites In: Proceedings of the IEEE/CVF international conference on computer vision.

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis In: Proceedings of the IEEE/CVF international conference on computer vision

Reference 15

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Observation c853280d-c540-4c26-b224-96eca97dd16c · outbound

This paper cites Decoupled Weight Decay Regularization.

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis Decoupled Weight Decay Regularization

Reference 16

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Observation 639d4407-d902-4daf-aa24-fcb9ad6cdfeb · outbound

This paper cites The lancet 362(9392), 1295–1304 (2003).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis The lancet 362(9392), 1295–1304 (2003)

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 2116fb21-fed2-4055-a304-7914931a7c6d · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis Attention U-Net: Learning Where to Look for the Pancreas

Reference 18

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Observation 37d61706-2299-44b7-935f-7c5cc7e6f0fd · outbound

This paper cites In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, Oc- tober 5-9, 2015, proceedings, part III 18.

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, Oc- tober 5-9, 2015, proceedings, part III 18

Reference 19

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Observation cf4b48f9-279f-4fb9-b865-eac89e1c4e08 · outbound

This paper cites PLoS medicine 12(12), e1001920 (2015).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis PLoS medicine 12(12), e1001920 (2015)

Reference 20

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Observation b6263057-a376-4360-bec0-bdc9af59cb10 · outbound

This paper cites PLoS neglected tropical diseases 4(6), e722 (2010).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis PLoS neglected tropical diseases 4(6), e722 (2010)

Reference 21

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Observation 53086d06-ad19-4142-9e6c-beaccd4f9ea7 · outbound

This paper cites Advances in Neural Information Processing Systems (2017).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis Advances in Neural Information Processing Systems (2017)

Reference 22

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Observation 9d6fec0e-0bf0-4e46-9528-cdeae4ff40d5 · outbound

This paper cites The Lancet Digital Health 5(11), e754–e762 (2023).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis The Lancet Digital Health 5(11), e754–e762 (2023)

Reference 23

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 36e4b420-98f6-48ba-93bd-baff6a42cf92 · outbound

This paper cites In: Proceedings of the European conference on computer vision (ECCV).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis In: Proceedings of the European conference on computer vision (ECCV)

Reference 24

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Observation fc114573-ab1a-4153-8fbe-56d4e9940021 · outbound

This paper cites Pattern Recognition 143, 109819 (2023).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis Pattern Recognition 143, 109819 (2023)

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation b8259df2-76c5-4980-a754-9dfe2d05971b · outbound

This paper cites In: ICLR 2025 (2025).

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis In: ICLR 2025 (2025)

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation b2cee5ea-42b2-4894-98f9-d823f9fb7938 · outbound

This paper cites HMT-UNet: A hybird Mamba-Transformer Vision UNet for Medical Image Segmentation.

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis HMT-UNet: A hybird Mamba-Transformer Vision UNet for Medical Image Segmentation

Reference 27

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Observation 9b5cf6fa-0a90-4909-a995-d484b6db7134 · outbound

This paper cites an unresolved cited work.

EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis Unresolved cited work

Reference 28

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Pith citing papers

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