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.

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

pith.paper-citation-record.v1
2502.02418 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:17:42.251488Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

32 of 32 outbound references displayed

verified exact1
verified fuzzy30
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.671206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.661391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.651009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.640132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.619495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.609242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.599222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.588294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.577381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.568556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.558734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.548445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.538388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.527887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.516983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9a685a00-20bf-4be4-b8ad-00c96d9bd29d · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.506036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified exact; local_arxiv, observed 2026-08-09T12:17:42.356283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ed2b40fd-5997-41cc-a4c6-9b494bccd6b4 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.495503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.485540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.474608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a43e45b3-4da9-4f78-a14b-00248b92cfbc · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.464474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a0691b87-8b94-4ed9-85c2-62275464eebd · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-09T12:17:42.224735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2199d58a-d289-4768-98b1-2168dd4ad51a · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.453782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a82650c1-fb72-424b-a327-f1ec53bd5db8 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.444605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 20eaef58-1573-434c-8c3b-73de2bcd60ce · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.435390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 82efeded-f7f3-4805-b908-39d2a1f9261f · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.425791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0164f935-58b3-4968-b90f-9dcc59de732e · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.414424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.402323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:17:42.379815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 11d0a710-1688-43aa-be32-e8d1eeecd497 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.