Paper Citation Record · LEDGER

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems

As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2502.02418.

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pith.paper-citation-record.v1
2502.02418 v1

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Pith citing papers itemized under the disclosed page cap.

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Outbound references

Observation 7ab8708f-2111-4872-99b5-de6a8b8ff456 · outbound

This paper cites Deep learning for tomographic image reconstruction,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Deep learning for tomographic image reconstruction,

Reference 1

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Observation 22ecc243-9eca-48cb-8c1f-d48639dae1b8 · outbound

This paper cites Deep learning for biomedical image reconstruction: A survey,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Deep learning for biomedical image reconstruction: A survey,

Reference 2

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Observation b4e9a558-426e-4681-abc1-990d38d12ae1 · outbound

This paper cites Image reconstruction by domain- transform manifold learning,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Image reconstruction by domain- transform manifold learning,

Reference 3

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Observation ce8a5c83-24da-425c-b587-8bd2aae28c12 · outbound

This paper cites Learning to reconstruct computed tomography images directly from sinogram data under a variety of data acquisition conditions,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Learning to reconstruct computed tomography images directly from sinogram data under a variety of data acquisition conditions,

Reference 4

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Observation 060cd5d6-771b-494d-9979-79485b1894f8 · outbound

This paper cites DeepPET: A deep encoder –decoder network for directly solving the PET image reconstruction inverse problem,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems DeepPET: A deep encoder –decoder network for directly solving the PET image reconstruction inverse problem,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.629591Z

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Observation 4a2d907f-da88-46be-af94-423209310e3e · outbound

This paper cites DirectPET: full -size neural network PET reconstruction from sinogram data,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems DirectPET: full -size neural network PET reconstruction from sinogram data,

Reference 6

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Observation 1a8e6564-5e55-44ce-899d-06cab293e42e · outbound

This paper cites A learned reconstruction network for SPECT imaging,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems A learned reconstruction network for SPECT imaging,

Reference 7

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Observation f5994581-4fa9-49c8-ac38-7bf027b577c9 · outbound

This paper cites Deep embedding- attention-refinement for sparse-view CT reconstruction,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Deep embedding- attention-refinement for sparse-view CT reconstruction,

Reference 8

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Observation d32525f4-a14b-4fb4-8c17-cd934bc50396 · outbound

This paper cites WNet: A data-driven dual-domain denoising model for sparse- view computed tomography with a trainable reconstruction layer,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems WNet: A data-driven dual-domain denoising model for sparse- view computed tomography with a trainable reconstruction layer,

Reference 9

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Observation 5c61f8ab-4b43-4237-a6b2-c973bef9d630 · outbound

This paper cites Direct reconstruction for simultaneous dual- tracer PET imaging based on multi- task learning,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Direct reconstruction for simultaneous dual- tracer PET imaging based on multi- task learning,

Reference 10

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Observation ea7c5181-81a6-4ae4-8dd7-70faabf6164c · outbound

This paper cites ReconU-Net: A direct PET image reconstruction using U- Net architecture with back projection-induced skip connection,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems ReconU-Net: A direct PET image reconstruction using U- Net architecture with back projection-induced skip connection,

Reference 11

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Observation 410845c9-4a32-48e9-97ad-93cf84271011 · outbound

This paper cites On instabilities of deep learning in image reconstruction and the potential costs of AI,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems On instabilities of deep learning in image reconstruction and the potential costs of AI,

Reference 12

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Observation f76f41d9-2da6-4423-9913-de44b290985d · outbound

This paper cites A review of deep learning CT reconstruction: Concepts, limitations, and promise in clinical practice,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems A review of deep learning CT reconstruction: Concepts, limitations, and promise in clinical practice,

Reference 13

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Observation 1656562f-5eb4-4946-8e57-0408432be59d · outbound

This paper cites Geometric deep learning and equivariant neural networks,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Geometric deep learning and equivariant neural networks,

Reference 14

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Observation 0d517cfd-ff24-41be-ba77-4ccc78c21ff7 · outbound

This paper cites Harmonic networks: Deep translation and rotation equivariance,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Harmonic networks: Deep translation and rotation equivariance,

Reference 15

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Observation ba832e00-307c-4711-94b0-2096d84ed1b9 · outbound

This paper cites Robust equivariant imaging: A fully unsupervised framework for learning to image fro m noisy and partial measurements,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Robust equivariant imaging: A fully unsupervised framework for learning to image fro m noisy and partial measurements,

Reference 16

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This paper cites Provably strict generalisation benefit for equivariant models,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Provably strict generalisation benefit for equivariant models,

Reference 17

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Observation 51b386eb-3b33-4fe7-9b11-0ba7151c2ea8 · outbound

This paper cites Rotation-Equivariant Deep Learning for Diffusion MRI.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Rotation-Equivariant Deep Learning for Diffusion MRI

Reference 18

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This paper cites Image classification based on CNN: A sur vey,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Image classification based on CNN: A sur vey,

Reference 19

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Observation 739a065f-0f09-4e8d-a527-6ba54c9c48bb · outbound

This paper cites Dense steerable filter CNNs for exploiting rotational symmetry in histology images,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Dense steerable filter CNNs for exploiting rotational symmetry in histology images,

Reference 20

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Observation 61aa20d7-408d-483a-b637-24acd6c4db30 · outbound

This paper cites Pulmonary nodule detection in CT scans with equivariant CNNs,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Pulmonary nodule detection in CT scans with equivariant CNNs,

Reference 21

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This paper cites Roto -translation equivariant convolutional networks: Application to histopathology image analysis,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Roto -translation equivariant convolutional networks: Application to histopathology image analysis,

Reference 22

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This paper cites Spherical CNN for medical imaging applications: Importance of equivariance in image recon struction and denoising,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Spherical CNN for medical imaging applications: Importance of equivariance in image recon struction and denoising,

Reference 23

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This paper cites SE(3)- equivariant and noise- invariant 3D rigid motion tracking in brain MRI,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems SE(3)- equivariant and noise- invariant 3D rigid motion tracking in brain MRI,

Reference 24

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This paper cites Equivariant neural networks for inverse problems,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Equivariant neural networks for inverse problems,

Reference 25

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This paper cites Equivariant plug -and-play image reconstruction,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Equivariant plug -and-play image reconstruction,

Reference 26

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This paper cites Twenty new digital brain phantoms for creation of validation image data bases,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Twenty new digital brain phantoms for creation of validation image data bases,

Reference 27

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This paper cites Implementing and accelerating the EM algorithm for positron emission tomography,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Implementing and accelerating the EM algorithm for positron emission tomography,

Reference 28

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Observation ab976602-081e-41f8-a155-4e89aff9cc77 · outbound

This paper cites SVD for imaging systems with discrete rotational symmetry,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems SVD for imaging systems with discrete rotational symmetry,

Reference 29

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Observation bb8efa7c-da71-4ccd-b031-3277e508c1c8 · outbound

This paper cites Fully -3D PET image reconstruction using scanner -independent, adaptive projection data and highly rotation - symmetric voxel assemblies,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Fully -3D PET image reconstruction using scanner -independent, adaptive projection data and highly rotation - symmetric voxel assemblies,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.390979Z

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Observation e81ca736-a5ae-478b-99a4-3c9de0e89282 · outbound

This paper cites Monte- Carlo system modeling for PET reconstruction: A rotator approach,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Monte- Carlo system modeling for PET reconstruction: A rotator approach,

Reference 31

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This paper cites Discrete imaging models for three- dimensional optoacoustic tomography using radially symmetric expansion functions,.

Incorporating Cyclic Group Equivariance into Deep Learning for Reliable Reconstruction of Rotationally Symmetric Tomography Systems Discrete imaging models for three- dimensional optoacoustic tomography using radially symmetric expansion functions,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.367878Z

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Pith citing papers

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