Paper Citation Record · LEDGER

ViM-UNet: Vision Mamba for Biomedical Segmentation

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2404.07705.

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

pith.paper-citation-record.v1
2404.07705 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:42:23.003578Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-01T06:45:29.641891Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 07f10dda-bbed-40d6-b4c4-bab66c0e1dc3 · inbound

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis cites this paper.

A Study on Context Length and Efficient Transformers for Biomedical Image Analysis ViM-UNet: Vision Mamba for Biomedical Segmentation

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T22:50:08.376579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e50d0c4a-36c5-46c4-96ea-078c94eaa180 · inbound

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation cites this paper.

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation ViM-UNet: Vision Mamba for Biomedical Segmentation

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-16T10:42:23.003578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd0451a4-2b35-471c-ad99-d95a2131018f · inbound

APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms cites this paper.

APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms ViM-UNet: Vision Mamba for Biomedical Segmentation

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:14:19.420270Z

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 3c7ae9f0-b96d-40f7-93a9-2abc8487a508 · inbound

APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms cites this paper.

APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms ViM-UNet: Vision Mamba for Biomedical Segmentation

Reference 137

Resolution: verified exact; arxiv_id, observed 2026-07-01T06:45:29.644000Z

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.