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Paper Citation Record · LEDGER

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification

As of 11 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2601.19743.

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pith.paper-citation-record.v1
2601.19743 v3

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measured 41 of 41 reference resolution

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Outbound references

Observation bc9cf621-6c0a-42fb-a7c9-7a1d71c0062c · outbound

This paper cites Nature Reviews Cardiology17(9), 559–573 (2020).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Nature Reviews Cardiology17(9), 559–573 (2020)

Reference 1

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This paper cites Nature Reviews Cardiology13(6), 368–378 (2016).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Nature Reviews Cardiology13(6), 368–378 (2016)

Reference 2

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Observation dedbdeda-f158-4ed4-a37f-b7f4baae286a · outbound

This paper cites Circulation research119(2), 357–374 (2016).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Circulation research119(2), 357–374 (2016)

Reference 3

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This paper cites Journal of Nuclear Medicine56(Supplement 4), 31–38 (2015).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Journal of Nuclear Medicine56(Supplement 4), 31–38 (2015)

Reference 4

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Observation 78f0d8ee-7bac-4d25-8318-2ade1e88c549 · outbound

This paper cites Journal of the American College of Cardiology79(17), 263–421 (2022).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Journal of the American College of Cardiology79(17), 263–421 (2022)

Reference 5

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This paper cites Journal of the American College of Cardiology83(15), 1444–1488 (2024).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Journal of the American College of Cardiology83(15), 1444–1488 (2024)

Reference 6

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This paper cites Jama329(10), 827–838 (2023).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Jama329(10), 827–838 (2023)

Reference 7

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Observation 6ee846b8-a920-4c0b-b6d9-449d4d0ff4e0 · outbound

This paper cites The international journal of cardiovascular imaging31(7), 1303–1314 (2015) 23.

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification The international journal of cardiovascular imaging31(7), 1303–1314 (2015) 23

Reference 8

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This paper cites The Lancet357(9262), 1107–1117 (2001).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification The Lancet357(9262), 1107–1117 (2001)

Reference 9

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This paper cites European Journal of Heart Failure23(4), 578–589 (2021).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification European Journal of Heart Failure23(4), 578–589 (2021)

Reference 10

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Observation 6d8ab265-29c0-4d93-ba43-fe366e6995af · outbound

This paper cites Journal of echocardiography17(1), 10–16 (2019).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Journal of echocardiography17(1), 10–16 (2019)

Reference 11

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This paper cites Journal of the American College of Cardiology66(13), 1456–1466 (2015).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Journal of the American College of Cardiology66(13), 1456–1466 (2015)

Reference 12

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This paper cites The international journal of cardiovascular imaging26(1), 57–64 (2010).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification The international journal of cardiovascular imaging26(1), 57–64 (2010)

Reference 13

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This paper cites Echocardiography38(4), 582– 589 (2021).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Echocardiography38(4), 582– 589 (2021)

Reference 14

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This paper cites Journal of the American Heart Association13(8), 032257 (2024).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Journal of the American Heart Association13(8), 032257 (2024)

Reference 15

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

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp

Reference 16

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Observation 6dd0b623-136f-4534-b381-9faa66b96941 · outbound

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

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)

Reference 17

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This paper cites Nature580(7802), 252–256 (2020).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Nature580(7802), 252–256 (2020)

Reference 18

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This paper cites Journal of big Data8(1), 53 (2021).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Journal of big Data8(1), 53 (2021)

Reference 19

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This paper cites Journal of Cardiovascular Imaging29(3), 193 (2021).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Journal of Cardiovascular Imaging29(3), 193 (2021)

Reference 20

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This paper cites Journal of Visual Communication and Image Representation41, 406–413 (2016).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Journal of Visual Communication and Image Representation41, 406–413 (2016)

Reference 21

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This paper cites Journal of Visual Communication and Image Representation90, 103685 (2023).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Journal of Visual Communication and Image Representation90, 103685 (2023)

Reference 22

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This paper cites NPJ digital medicine5(1), 156 (2022).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification NPJ digital medicine5(1), 156 (2022)

Reference 23

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This paper cites Journal of Visual Communication and Image Representation 70, 102749 (2020).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Journal of Visual Communication and Image Representation 70, 102749 (2020)

Reference 24

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This paper cites IEEE Transactions on Multimedia22(7), 1744–1755 (2020).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification IEEE Transactions on Multimedia22(7), 1744–1755 (2020)

Reference 25

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This paper cites IEEE journal of biomedical and health informatics26(3), 1128– 1139 (2021).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification IEEE journal of biomedical and health informatics26(3), 1128– 1139 (2021)

Reference 26

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This paper cites GUSL: A Novel and Efficient Machine Learning Model for Prostate Segmentation on MRI.

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification GUSL: A Novel and Efficient Machine Learning Model for Prostate Segmentation on MRI

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Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification JAMA cardiology6(6), 624–632 (2021)

Reference 28

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This paper cites BMC medical informatics and decision making20(1), 310 (2020).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification BMC medical informatics and decision making20(1), 310 (2020)

Reference 29

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Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp

Reference 30

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Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Wiley interdisciplinary reviews: computational statistics2(4), 433–459 (2010)

Reference 31

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Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification American heart journal146(3), 388–397 (2003)

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Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification IEEE transactions on pattern analysis and machine intelligence37(9), 1904–1916 (2015)

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Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Network In Network

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Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification In: 2016 Fourth International Conference on 3D Vision (3DV), pp

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Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp

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Observation 013c28f4-159f-4717-bae0-1a2318d1556f · outbound

This paper cites Nature methods18(2), 203–211 (2021).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Nature methods18(2), 203–211 (2021)

Reference 37

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Observation c03ea751-cf7e-4c72-a1ca-7652ab11e5f3 · outbound

This paper cites Clinical Physiology and Functional Imaging41(5), 443–451 (2021).

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Clinical Physiology and Functional Imaging41(5), 443–451 (2021)

Reference 38

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Observation 7670f0c9-a013-4677-bf50-2c5b28825f49 · outbound

This paper cites data from the hunt study.

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification data from the hunt study

Reference 39

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Observation be00b5c9-37ec-44f2-b8dd-f0a46e17fd47 · outbound

This paper cites an unresolved cited work.

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification Unresolved cited work

Reference 40

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Observation 71edbe91-f199-4ecf-94bd-070bc44c70e6 · outbound

This paper cites npj Digital Medicine8(1), 341 (2025) 26.

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification npj Digital Medicine8(1), 341 (2025) 26

Reference 41

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