Paper Citation Record · LEDGER

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation

As of 19 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2608.12196.

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

pith.paper-citation-record.v1
2608.12196 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:17:32.530930Z

measured 34 of 34 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

34 of 34 outbound references displayed

verified exact0
verified fuzzy25
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 887e51ed-f5c3-4a19-bd35-18dd02dcd294 · outbound

This paper cites E., et al.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation E., et al

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.938896Z

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 5e6df7cd-5aac-4ba7-9278-fd6df685502b · outbound

This paper cites F., Li, H.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation F., Li, H

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.926547Z

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 9d10251c-2790-4612-958c-de2107d2bda6 · outbound

This paper cites H., et al.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation H., et al

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.915353Z

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 7313fb62-3cf5-4033-8cb1-091e90dfe34e · outbound

This paper cites H., Jakab, A., Bauer, S., et al.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation H., Jakab, A., Bauer, S., et al

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.903822Z

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 b32b0c39-89d8-45a9-8278-fc68d546d87f · outbound

This paper cites U-Net: Convolutional networks for biomedical image segmentation.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation U-Net: Convolutional networks for biomedical image segmentation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.892287Z

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 214e7276-0d1f-4413-8b1b-0e2c974dc5f0 · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation Attention U-Net: Learning Where to Look for the Pancreas

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc79a6d6-4948-408d-9ba1-0d01f4b956cd · outbound

This paper cites an unresolved cited work.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:17:32.880861Z

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 cfed8c9b-562a-42d2-9a2e-d8e09217c101 · outbound

This paper cites F., Kohl, S.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation F., Kohl, S

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.869296Z

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 dcc94551-180b-49ed-ba90-0090b41c2b9d · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T00:17:32.442204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 689ce2ba-9dd1-4d01-9ae0-d201999e0228 · outbound

This paper cites Swin-UNet: Unet-like pure transformer for medical image segmentation.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation Swin-UNet: Unet-like pure transformer for medical image segmentation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.857744Z

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 d4e360c3-472d-4c3c-99b6-d7bf3b063982 · outbound

This paper cites InCVPR, pp.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation InCVPR, pp

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.846003Z

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 92ab641f-0f87-48d4-8d22-904b6566b7ed · outbound

This paper cites S., Brox, T., & Ronneberger, O.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation S., Brox, T., & Ronneberger, O

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.836106Z

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 e15ce2fd-fb89-424f-855f-562fdc08cb2c · outbound

This paper cites an unresolved cited work.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:17:32.824537Z

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 6ed6ec10-8898-48bd-a16d-321a93e33787 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T00:17:32.459597Z

Source-reported events for the cited work

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Observation ed1eea42-a9c1-434f-9d98-4c9062dd8005 · outbound

This paper cites Swin Transformer: Hierarchical vision transformer using shifted windows.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation Swin Transformer: Hierarchical vision transformer using shifted windows

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.814312Z

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 6a5e49b3-c578-4f10-b60a-04b330155ddb · outbound

This paper cites UNETR: Transformers for 3D medical image segmentation.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation UNETR: Transformers for 3D medical image segmentation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.803978Z

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 180d15cb-fccb-4011-8165-1c942052e736 · outbound

This paper cites MISSFormer: An Effective Medical Image Segmentation Transformer.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T00:17:32.469284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29d82ddc-883c-477b-96e8-ab2754f58e6b · outbound

This paper cites DcT: A dice loss based cross-attention vision transformer for medical image segmentation.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation DcT: A dice loss based cross-attention vision transformer for medical image segmentation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.792706Z

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 7523e7dd-5f14-407a-ba74-30ea1abdd304 · outbound

This paper cites Squeeze-and-excitation networks.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation Squeeze-and-excitation networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.781743Z

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 0d1302c6-98be-4145-a276-ff91591a6644 · outbound

This paper cites Y., & So Kweon, I.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation Y., & So Kweon, I

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.770886Z

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 ad2b5eb7-18e7-4096-894f-7fd38614253b · outbound

This paper cites K., Rauland, A., et al.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation K., Rauland, A., et al

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.759329Z

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 6e521544-8d0d-480f-89da-f9b78776ab77 · outbound

This paper cites E., Kevrekidis, I.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation E., Kevrekidis, I

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.749537Z

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 e9a0b322-1432-46f1-bf61-bcfdba2490a1 · outbound

This paper cites an unresolved cited work.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:17:32.736825Z

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 0d367bbf-66a2-4472-9e00-2ad6b42bd28f · outbound

This paper cites A volumetric transformer for accurate 3D tumor segmentation in CT scans.Applied Sciences, 12(11):5642, 2022.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation A volumetric transformer for accurate 3D tumor segmentation in CT scans.Applied Sciences, 12(11):5642, 2022

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.724472Z

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 9ea87896-dae7-444e-a0c3-a307b2143660 · outbound

This paper cites Source-relaxed domain adaptation for image segmentation.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation Source-relaxed domain adaptation for image segmentation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.712921Z

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 030c576f-5ab0-44c5-a86a-dc06fe369d00 · outbound

This paper cites an unresolved cited work.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:17:32.701813Z

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 9fb7715f-f897-45bc-aedc-f7be6474e310 · outbound

This paper cites Texture classification based on spectrum and rank features.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation Texture classification based on spectrum and rank features

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.690600Z

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 261817a4-4fd5-4d1c-aeab-b076545f8863 · outbound

This paper cites C., Sheikh, H.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation C., Sheikh, H

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.679274Z

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 307627c6-a3ef-4a80-8bd1-e71c960302df · outbound

This paper cites Invariant measures of image features from phase information.PhD Thesis, University of Western Australia, 1996.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation Invariant measures of image features from phase information.PhD Thesis, University of Western Australia, 1996

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.668187Z

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 421500cd-ac1f-486c-a5e9-f8556241c95d · outbound

This paper cites an unresolved cited work.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:17:32.656327Z

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 fe437230-2b78-4c4d-b4c3-cf1c347748dc · outbound

This paper cites I., Osher, S., & Fatemi, E.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation I., Osher, S., & Fatemi, E

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.645776Z

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 8de0c06c-bc2e-4e78-9a01-c9e4354f99b0 · outbound

This paper cites Leveraging matrix invertibility as features in neural networks for medical image segmentation.In preparation, 2024.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation Leveraging matrix invertibility as features in neural networks for medical image segmentation.In preparation, 2024

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.633400Z

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 6539cae6-27ce-4367-b019-7b21a595601e · outbound

This paper cites W., & Sun, J.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation W., & Sun, J

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.620883Z

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 5c5b64a7-12a9-4dae-8d79-1d63137f8bdf · outbound

This paper cites 2.5D lightweight RIU-Net for automatic liver and tumor segmentation from CT.Biomedical Signal Processing and Control, 75:103567, 2022.

M-Net: Integrating Spectral Features and Physical Field Operators into Deep Learning for Medical Image Segmentation 2.5D lightweight RIU-Net for automatic liver and tumor segmentation from CT.Biomedical Signal Processing and Control, 75:103567, 2022

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:17:32.607666Z

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.