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Paper Citation Record · LEDGER

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification

As of 8 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2607.29462.

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

pith.paper-citation-record.v1
2607.29462 v2

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measured 38 of 38 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-04T03:20:32.974731Z

measured 38 of 38 standing notices

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measured 0 of 1 external citation measurements

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Reference resolution

38 of 38 outbound references displayed

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Outbound references

Observation 3b5e5f8b-87b9-4c9a-a9e1-bac42c87248b · outbound

This paper cites Data in Brief30, 105474 (2020).

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Data in Brief30, 105474 (2020)

Reference 1

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This paper cites Data in Brief28, 104863 (2020).

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Data in Brief28, 104863 (2020)

Reference 2

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This paper cites Medical Image Analysis84, 102680 (2023).

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Medical Image Analysis84, 102680 (2023)

Reference 3

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This paper cites 2021 IEEE/CVF International Conference on Computer Vision (ICCV) pp.

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification 2021 IEEE/CVF International Conference on Computer Vision (ICCV) pp

Reference 4

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This paper cites Nature medicine30(3), 850–862 (Mar 2024).

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Nature medicine30(3), 850–862 (Mar 2024)

Reference 5

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This paper cites Advances in Neural Information Processing Systems (NeurIPS)35(2022).

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Advances in Neural Information Processing Systems (NeurIPS)35(2022)

Reference 6

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Unresolved cited work

Reference 7

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This paper cites In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

Reference 8

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Observation 8d204d6e-69c8-449f-ac1c-38b24f5295b4 · outbound

This paper cites Doerrich et al.

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Doerrich et al

Reference 9

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This paper cites MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions.

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions

Reference 10

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This paper cites Scientific Reports15(1), 7669 (Mar 2025).

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Scientific Reports15(1), 7669 (Mar 2025)

Reference 11

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This paper cites ICML’24, JMLR.org (2024).

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification ICML’24, JMLR.org (2024)

Reference 12

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Unresolved cited work

Reference 13

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Neural Computation3(1), 79–87 (Feb 1991)

Reference 14

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Unresolved cited work

Reference 15

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This paper cites In: proceedings of Medical Image Com- puting and Computer Assisted Intervention – MICCAI 2024.

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification In: proceedings of Medical Image Com- puting and Computer Assisted Intervention – MICCAI 2024

Reference 16

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This paper cites PLOS Medicine16, e1002730 (2019).

MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification PLOS Medicine16, e1002730 (2019)

Reference 17

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Cell 172, 1122–1131.e9 (2018)

Reference 18

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Observation 99d56a39-d434-4406-922c-c96778375279 · outbound

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification In: International Conference on Learning Representations (ICLR) (2023)

Reference 19

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Patterns3, 100512 (2022)

Reference 20

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification In: ICLR (2024)

Reference 21

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification In: Meila, M., Zhang, T

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Nature Biomedical Engineering6(12), 1399–1406 (Sep 2022)

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Scientific Data 2018 5:15, 1–9 (2018)

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification IEEE Transactions on Medical Imaging 38(8), 1885–1898 (2019)

Reference 34

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Scientific Data10(1), 41 (Jan 2023)

Reference 35

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification In: International Conference on Learning Representations (ICLR) (2024)

Reference 37

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MoPET: Parameter-Efficient Mixture-of-Experts for Unified Medical Image Classification Isolated PEFT

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T03:20:32.974731Z digest=sha256:f5dad42c9993983ef3a0fa101672d5f180bfa1719fd776c87b119b0cdc14e60b

Pith citing papers

No inbound Pith citation observations are available.