Paper Citation Record · LEDGER

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis

As of 16 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2507.16267.

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

pith.paper-citation-record.v1
2507.16267 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:18:52.907193Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

37 of 37 outbound references displayed

verified exact0
verified fuzzy33
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c25bc2d8-f8c7-482c-aff3-ba4127d3704e · outbound

This paper cites ”Alzheimer’s disease facts and figures.” Alzheimers Dement 19.4 (2023): 1598-1695.

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis ”Alzheimer’s disease facts and figures.” Alzheimers Dement 19.4 (2023): 1598-1695

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:01.678672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2a4b5206-54e4-4150-af0a-867dd754cb38 · outbound

This paper cites Clinical trials of new drugs for Alzheimer disease[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Clinical trials of new drugs for Alzheimer disease[J]

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:01.375631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0e964de0-bf36-4421-b33c-750859a62954 · outbound

This paper cites The clinical use of structural MRI in Alzheimer disease[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis The clinical use of structural MRI in Alzheimer disease[J]

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:01.088102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ac19873e-2f33-436e-b59a-b743e3cf58c4 · outbound

This paper cites Convolutional neural networks for classification of Alzheimer’s disease: Overview and re- producible evaluation[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Convolutional neural networks for classification of Alzheimer’s disease: Overview and re- producible evaluation[J]

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:00.903334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7d7add47-fe73-4a7a-8102-309a2bb231e7 · outbound

This paper cites Residual and plain con- volutional neural networks for 3D brain MRI classification[C]//2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017).

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Residual and plain con- volutional neural networks for 3D brain MRI classification[C]//2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:00.652432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6badd26e-0c82-49d6-b79b-5a4325dc109a · outbound

This paper cites U-net based analysis of MRI for Alzheimer’s disease diagnosis[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis U-net based analysis of MRI for Alzheimer’s disease diagnosis[J]

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:00.359445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ff524695-8967-4a9b-84b7-203215c0f5e4 · outbound

This paper cites Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease[J]

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:19:00.106250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c46805b3-28dc-4cac-89cf-84fc04445a40 · outbound

This paper cites Arm-net: Attention-guided residual multiscale cnn for multiclass brain tumor classification using mr images[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Arm-net: Attention-guided residual multiscale cnn for multiclass brain tumor classification using mr images[J]

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:59.658590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4eec54c7-c6b7-4ada-899a-4312a4c2a127 · outbound

This paper cites MPS-FFA: A multiplane and multiscale feature fusion attention network for Alzheimer’s disease prediction with structural MRI[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis MPS-FFA: A multiplane and multiscale feature fusion attention network for Alzheimer’s disease prediction with structural MRI[J]

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:59.433249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6d5f9e20-3194-4127-8ff3-6e1c7b30e10b · outbound

This paper cites MSFNet-2SE: A multi-scale fusion convolutional network for Alzheimer’s disease classification on magnetic resonance images[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis MSFNet-2SE: A multi-scale fusion convolutional network for Alzheimer’s disease classification on magnetic resonance images[J]

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:59.218624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b020f803-8f87-4cc4-b02f-3de3b3ea573d · outbound

This paper cites MACFNet: Detection of Alzheimer’s dis- ease via multiscale attention and cross-enhancement fusion network[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis MACFNet: Detection of Alzheimer’s dis- ease via multiscale attention and cross-enhancement fusion network[J]

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:58.970039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b59f121e-2203-4134-8049-de479e7b46a4 · outbound

This paper cites Kolmogorov-Arnold Networks for Time Series Granger Causality Inference.

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Kolmogorov-Arnold Networks for Time Series Granger Causality Inference

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T15:18:49.793139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4b4a320-8f48-4c8c-890a-3f6620ea545d · outbound

This paper cites A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes.

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processes

Reference 14

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T15:18:53.132218Z

Source-reported events for the cited work

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Observation 5dfe3d51-4bf8-4bf2-8dbf-b49a9b4b2b4e · outbound

This paper cites Attention is all you need[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Attention is all you need[J]

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:58.699018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 983eb93d-755d-4153-a5da-f9de11c6b3e6 · outbound

This paper cites ”Transformers in vision: A survey.” ACM comput- ing surveys (CSUR) 54.10s (2022): 1-41.

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis ”Transformers in vision: A survey.” ACM comput- ing surveys (CSUR) 54.10s (2022): 1-41

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:58.508533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c74ce583-187a-4e96-8f63-cdad5bf4469c · outbound

This paper cites ”End-to-end object detection with transformers.” European conference on computer vision.

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis ”End-to-end object detection with transformers.” European conference on computer vision

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:58.193308Z

Source-reported events for the cited work

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Observation c912b57c-fc93-436a-940c-e8e500a3e1ef · outbound

This paper cites Efficient self-attention mechanism and struc- tural distilling model for Alzheimer’s disease diagnosis[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Efficient self-attention mechanism and struc- tural distilling model for Alzheimer’s disease diagnosis[J]

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:57.956057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 266460d2-f94d-4fbe-8a93-46ae2f1773c0 · outbound

This paper cites Trans-resnet: Integrating transformers and cnns for alzheimer’s disease classification[C]//2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI).

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Trans-resnet: Integrating transformers and cnns for alzheimer’s disease classification[C]//2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:57.709416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dc021b2b-c370-4ad4-b762-58cd22880d7c · outbound

This paper cites M3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis M3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:57.465865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ff6d7298-b4c0-40a0-bc2d-a65db2d1554f · outbound

This paper cites Conv-Swinformer: Integration of CNN and shift window attention for Alzheimer’s disease classification[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Conv-Swinformer: Integration of CNN and shift window attention for Alzheimer’s disease classification[J]

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:57.244760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 48710927-b34f-463b-9e5a-84dd203272d9 · outbound

This paper cites Diagnosis of Alzheimer’s disease via optimized lightweight convolution-attention and structural MRI[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Diagnosis of Alzheimer’s disease via optimized lightweight convolution-attention and structural MRI[J]

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:56.958669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 29b56b7b-5d98-44af-9c83-e07c1bfb2b15 · outbound

This paper cites MMTFN: Multi-modal multi-scale trans- former fusion network for Alzheimer’s disease diagnosis[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis MMTFN: Multi-modal multi-scale trans- former fusion network for Alzheimer’s disease diagnosis[J]

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:56.766151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e703c1c8-439d-4d08-b88b-d21e314d7938 · outbound

This paper cites Global filter networks for image classification[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Global filter networks for image classification[J]

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:56.490900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a1c8b4e5-d754-4d37-bf42-11bcea66a39e · outbound

This paper cites 3d global fourier network for alzheimer’s disease diagnosis using structural mri[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis 3d global fourier network for alzheimer’s disease diagnosis using structural mri[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:56.291150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3a2f6c68-8f57-47c6-8600-e84b29cca4a3 · outbound

This paper cites Addformer: Alzheimer’s disease detection from structural mri using fusion transformer[C]//2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI).

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Addformer: Alzheimer’s disease detection from structural mri using fusion transformer[C]//2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:56.032620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7052e565-13e0-4fcb-bddf-111f433a0e99 · outbound

This paper cites an unresolved cited work.

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-06T15:18:55.781779Z

Source-reported events for the cited work

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Observation cd32492c-2db8-486b-b444-247072d8e24d · outbound

This paper cites Automated brain extraction of multisequence MRI using artificial neural networks[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Automated brain extraction of multisequence MRI using artificial neural networks[J]

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:55.510802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f50ae62a-1992-4d37-943b-4d56175f8139 · outbound

This paper cites Densely connected convo- lutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Densely connected convo- lutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:55.274142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 09dd4d03-2786-4c26-9f11-9b35f10483d5 · outbound

This paper cites ECA-Net: Efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis ECA-Net: Efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:55.060901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 471ccdc6-7253-4a4e-808f-e65a3b94f00d · outbound

This paper cites Dual attention multi-instance deep learning for Alzheimer’s disease diagnosis with structural MRI[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Dual attention multi-instance deep learning for Alzheimer’s disease diagnosis with structural MRI[J]

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:54.815004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6d55c21d-5337-44f3-9ae4-13e2aa8d595a · outbound

This paper cites sMRI-PatchNet: A novel efficient explainable patch-based deep learning network for Alzheimer’s disease diagnosis with Structural MRI[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis sMRI-PatchNet: A novel efficient explainable patch-based deep learning network for Alzheimer’s disease diagnosis with Structural MRI[J]

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:54.563849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5a2c60b5-3289-4991-a57e-883b483856e9 · outbound

This paper cites Multi-relation graph convolutional network for Alzheimer’s disease diagnosis using structural MRI[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Multi-relation graph convolutional network for Alzheimer’s disease diagnosis using structural MRI[J]

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:54.292447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fa8c487e-4291-44a2-9afa-4c9febe07181 · outbound

This paper cites A hybrid multi-scale attention convolution and aging transformer network for Alzheimer’s disease diagnosis[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis A hybrid multi-scale attention convolution and aging transformer network for Alzheimer’s disease diagnosis[J]

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:59.886175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b40146cb-c9e5-4dd8-91a9-6f4ae27bff70 · outbound

This paper cites Interpretable medical deep framework by logits-constraint attention guiding graph-based multi-scale fusion for Alzheimer’s disease analysis[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Interpretable medical deep framework by logits-constraint attention guiding graph-based multi-scale fusion for Alzheimer’s disease analysis[J]

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:54.088708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d07dcdf2-482d-4635-8f8e-387117c31ba1 · outbound

This paper cites an unresolved cited work.

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis Unresolved cited work

Reference 36

Resolution: unresolved; raw_fallback, observed 2026-08-06T15:18:53.876094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5dfa2b67-3191-465a-ba50-4b280472cc7e · outbound

This paper cites A quantitatively interpretable model for Alzheimer’s disease prediction using deep counterfactuals[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis A quantitatively interpretable model for Alzheimer’s disease prediction using deep counterfactuals[J]

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:53.640931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c956dd3c-6e10-4f0f-9066-690f04868d48 · outbound

This paper cites CE-AH: A Contrast-Enhanced Attention Hierar- chical Network for Alzheimer’s Disease Diagnosis Based on Structural MRI[J].

SFNet: A Spatial-Frequency Domain Deep Learning Network for Efficient Alzheimer's Disease Diagnosis CE-AH: A Contrast-Enhanced Attention Hierar- chical Network for Alzheimer’s Disease Diagnosis Based on Structural MRI[J]

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:18:53.378367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.