Paper Citation Record · LEDGER

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis

As of 19 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2412.08896.

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

pith.paper-citation-record.v1
2412.08896 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:32:53.425728Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

41 of 41 outbound references displayed

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verified fuzzy39
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3e0b70ac-75f8-4def-ba9b-f5c33cdc4fd8 · outbound

This paper cites How common are the “common.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis How common are the “common

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:54.053773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f70fde5f-adc7-4dd2-839d-7a35b45a0c66 · outbound

This paper cites Global, regional, and national burden of epilepsy, 1990–2021: a systematic analysis for the global burden of disease study 2021,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Global, regional, and national burden of epilepsy, 1990–2021: a systematic analysis for the global burden of disease study 2021,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:54.039786Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bcad1392-c048-46a0-aec7-09079811b37b · outbound

This paper cites A review of signal processing and machine learning techniques for interictal epileptiform discharge detection,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis A review of signal processing and machine learning techniques for interictal epileptiform discharge detection,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:54.024106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0f95e7bf-3a48-4d56-8d57-0f4be5780912 · outbound

This paper cites MEG and EEG in epilepsy,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis MEG and EEG in epilepsy,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:54.009312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6127c4de-ebaf-4b35-b9bb-664ee0e5ca7f · outbound

This paper cites Development of expert-level automated detection of epileptiform discharges during Electroencephalogram interpretation,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Development of expert-level automated detection of epileptiform discharges during Electroencephalogram interpretation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.994642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f58ec236-9740-4882-b739-333fb68f06aa · outbound

This paper cites Automatic detection of interictal epileptiform discharges based on time-series sequence merging method,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Automatic detection of interictal epileptiform discharges based on time-series sequence merging method,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.981205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5355c85b-228f-43f2-81cb-56d3bf5835f3 · outbound

This paper cites Visual and automatic investigation of epileptiform spikes in intracranial EEG recordings,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Visual and automatic investigation of epileptiform spikes in intracranial EEG recordings,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.967607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8603307e-a4d8-42ab-9a21-23245178886f · outbound

This paper cites Single-trial classification of MEG recordings,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Single-trial classification of MEG recordings,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.953397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cb5b6f63-81e4-49b7-9332-052a64e2de01 · outbound

This paper cites EMS-Net: A deep learning method for autodetecting epileptic Magnetoencephalography spikes,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis EMS-Net: A deep learning method for autodetecting epileptic Magnetoencephalography spikes,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.939756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 567ed36b-3ec2-47bf-87c1-30598d25c2e6 · outbound

This paper cites Satelight: Self-attention-based model for epileptic spike detection from multi-electrode EEG.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Satelight: Self-attention-based model for epileptic spike detection from multi-electrode EEG

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.924925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c7de4ebe-2836-4cc2-b81a-e41e50a08796 · outbound

This paper cites Practical Fundamentals of Clinical MEG Interpretation in Epilepsy,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Practical Fundamentals of Clinical MEG Interpretation in Epilepsy,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.910213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a6d96e66-111c-426a-a5cb-f60dedd21cc1 · outbound

This paper cites Criteria for defining interictal epileptiform discharges in EEG,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Criteria for defining interictal epileptiform discharges in EEG,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.895554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 60a9f61e-0a5c-46d8-a460-376bd1180d30 · outbound

This paper cites V2IED: Dual-view learning framework for detecting events of interictal epileptiform discharges,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis V2IED: Dual-view learning framework for detecting events of interictal epileptiform discharges,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.880172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 35676dda-20a2-4342-9290-c6b400501768 · outbound

This paper cites CrossCon- vPyramid: Deep multimodal fusion for epileptic Magnetoencephalography spike detection,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis CrossCon- vPyramid: Deep multimodal fusion for epileptic Magnetoencephalography spike detection,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.864655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4849d326-5641-43f0-a7eb-f9d51da57a79 · outbound

This paper cites Automated detection of epileptic spikes and seizures incorporating a novel spatial clustering prior,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Automated detection of epileptic spikes and seizures incorporating a novel spatial clustering prior,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.849782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0f5d5add-acc1-4069-8687-02f6336f61ca · outbound

This paper cites Large brain model for learning generic representations with tremendous EEG data in BCI,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Large brain model for learning generic representations with tremendous EEG data in BCI,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.834673Z

Source-reported events for the cited work

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Observation 3c3ec506-f5c6-4796-ae03-6efdb864d8d5 · outbound

This paper cites EEGPT: Pretrained transformer for universal and reliable representation of eeg signals,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis EEGPT: Pretrained transformer for universal and reliable representation of eeg signals,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.819761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e5521ee8-d24a-4401-a1e9-22ee43540c18 · outbound

This paper cites Detection of interictal epileptiform discharges using transformer based deep neural network for patients with self-limited epilepsy with centrotemporal spikes,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Detection of interictal epileptiform discharges using transformer based deep neural network for patients with self-limited epilepsy with centrotemporal spikes,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.804461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4276943f-f9c2-4dc1-a5cc-a4c8c2376415 · outbound

This paper cites Toward a definition of MEG spike: Parametric description of spikes recorded simultaneously by MEG and depth electrodes,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Toward a definition of MEG spike: Parametric description of spikes recorded simultaneously by MEG and depth electrodes,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.790017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5a482507-0db7-448d-95f9-501c425e2673 · outbound

This paper cites Seizure occurrence and interspike interval: Telemetered Electroen- cephalogram studies,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Seizure occurrence and interspike interval: Telemetered Electroen- cephalogram studies,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.776529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c44d57ba-5363-4a6b-856e-b2e949f7c04e · outbound

This paper cites Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.762632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation baacfd8e-464c-479b-9098-20b7403089ec · outbound

This paper cites Spike detection: Inter-reader agreement and a statistical turing test on a large data set,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Spike detection: Inter-reader agreement and a statistical turing test on a large data set,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.748768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e30d07d6-b21f-4e61-a0cb-ecdd916712ca · outbound

This paper cites Deep residual learning for image recognition,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Deep residual learning for image recognition,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.734233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c39e46ea-ad9b-4d58-8391-44403c3d3125 · outbound

This paper cites BIOT: Biosignal transformer for cross-data learning in the wild,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis BIOT: Biosignal transformer for cross-data learning in the wild,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.720024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 568b5624-5ff9-49d8-ac90-e7745ede8df6 · outbound

This paper cites EEG2Rep: enhancing self-supervised EEG representation through informative masked inputs,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis EEG2Rep: enhancing self-supervised EEG representation through informative masked inputs,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.704848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8eb1ffae-2d9d-4bf4-9883-d99f6efeef3f · outbound

This paper cites The temple university hospital eeg data corpus,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis The temple university hospital eeg data corpus,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.689393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5b6d5159-76b1-40dd-950a-e23f2d7429bc · outbound

This paper cites Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.674035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a308cfa6-0d60-4d69-abcb-4f05c49b56b1 · outbound

This paper cites EMHapp: a pipeline for the automatic detection, localization and visualization of epileptic magnetoencephalographic high-frequency oscillations,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis EMHapp: a pipeline for the automatic detection, localization and visualization of epileptic magnetoencephalographic high-frequency oscillations,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.659788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2c26b754-5acb-4025-b388-ed552db6500d · outbound

This paper cites Batch normalization: accelerating deep network training by reducing internal covariate shift,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Batch normalization: accelerating deep network training by reducing internal covariate shift,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.643768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1bd6ae7d-c9f2-41ec-9db7-1d58d17800ae · outbound

This paper cites Gaussian error linear units (GELUs),.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Gaussian error linear units (GELUs),

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.629141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 773caeeb-65d4-45bb-903d-c5593ce2b267 · outbound

This paper cites Attention is all you need,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Attention is all you need,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.615181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cf3e72d7-4f40-4800-867e-bf01fca9b211 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Dropout: a simple way to prevent neural networks from overfitting,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.600338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ee1cad28-aee8-4ef6-98af-6365e5691548 · outbound

This paper cites Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.585577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d592112d-fc59-4db0-b993-91567d332cb7 · outbound

This paper cites Rethinking the Inception architecture for computer vision,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Rethinking the Inception architecture for computer vision,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.569127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e8987080-614d-4d7d-be3d-ce5af4f890a2 · outbound

This paper cites Energy-guided topology Mamba for EEG-based BCI,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Energy-guided topology Mamba for EEG-based BCI,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.553196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1e4a9b84-5dd3-44ea-870f-3766b35b6bbe · outbound

This paper cites Fully-Automated spike detection and dipole analysis of epileptic MEG using deep learning,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Fully-Automated spike detection and dipole analysis of epileptic MEG using deep learning,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.537928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d3c34720-8eac-48f4-94e7-74c01c95d31a · outbound

This paper cites KAN-EEG: towards replacing backbone-MLP for an effective seizure detection system,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis KAN-EEG: towards replacing backbone-MLP for an effective seizure detection system,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.522200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 80d9e0bf-c26a-43ef-81ac-8c4c399fc29c · outbound

This paper cites SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T17:32:53.411522Z

Source-reported events for the cited work

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Observation e05f94a3-8063-493c-a927-c0f8b55a87ed · outbound

This paper cites KAN: Kol- mogorov–arnold networks,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis KAN: Kol- mogorov–arnold networks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.506699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c9b99f3f-2538-4cf0-a489-3339f3e303ac · outbound

This paper cites Mamba: Linear-time sequence modeling with selective state spaces,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis Mamba: Linear-time sequence modeling with selective state spaces,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T17:32:53.421171Z

Source-reported events for the cited work

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Observation 83c378ff-b652-4b31-b28e-992755bea49c · outbound

This paper cites DeepUNet: A deep fully convolutional network for pixel-level sea-land segmentation,.

LV-CadeNet: A Long-View Feature Convolution-Attention Fusion Encoder-Decoder Network for EEG/MEG Spike Analysis DeepUNet: A deep fully convolutional network for pixel-level sea-land segmentation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:32:53.481712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.