Paper Citation Record · LEDGER

MISSFormer: An Effective Medical Image Segmentation Transformer

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2109.07162.

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

pith.paper-citation-record.v1
2109.07162 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:19:48.738394Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:46:56.578887Z

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 8d5f999b-0614-4ffe-87fb-482c9d187de6 · inbound

Primus: Enforcing Attention Usage for 3D Medical Image Segmentation cites this paper.

Primus: Enforcing Attention Usage for 3D Medical Image Segmentation MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:25:16.546747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6bf9a41e-39be-48ba-a77c-001051e016de · inbound

MSLAU-Net: A Hybrid CNN-Transformer Network for Medical Image Segmentation cites this paper.

MSLAU-Net: A Hybrid CNN-Transformer Network for Medical Image Segmentation MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-19T12:42:18.541881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 69e05224-4d5c-4f50-874c-3ea1a5e6741c · inbound

Med-URWKV{\dag}: Toward Enhanced Pretrained Pure VRWKV Models for Medical Image Segmentation cites this paper.

Med-URWKV{\dag}: Toward Enhanced Pretrained Pure VRWKV Models for Medical Image Segmentation MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:48.738394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e58199b-b903-4abe-97b1-f57ccffdebdf · inbound

InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation cites this paper.

InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T01:02:21.126176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24d22b1d-7d8b-4d31-8c1a-87bde9745855 · inbound

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation cites this paper.

MSA2-Net: Utilizing Self-Adaptive Convolution Module to Extract Multi-Scale Information in Medical Image Segmentation MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T12:33:52.207461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3379d49-fcaf-45b8-add0-1a6e1be60302 · inbound

SwinTextUNet: Integrating CLIP-Based Text Guidance into Swin Transformer U-Nets for Medical Image Segmentation cites this paper.

SwinTextUNet: Integrating CLIP-Based Text Guidance into Swin Transformer U-Nets for Medical Image Segmentation MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:11:05.345696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 999848bc-5242-491e-99a2-196b8e404dcf · inbound

RF-HiT: Rectified Flow Hierarchical Transformer for General Medical Image Segmentation cites this paper.

RF-HiT: Rectified Flow Hierarchical Transformer for General Medical Image Segmentation MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-11T13:06:05.688421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation edb7a17e-41c6-4c02-be2a-e90b4d5dee7b · inbound

SwInception -- Local Attention Meets Convolutions cites this paper.

SwInception -- Local Attention Meets Convolutions MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T08:33:15.461085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 264c4588-bb8b-42f4-a240-1ff4cef2af02 · inbound

MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models cites this paper.

MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:46:56.581066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 60cc9c9f-4a1a-4cf6-82fb-e01b559526a8 · 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 MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 99

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:14:19.430044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7499236f-a416-4d74-9636-094e27033846 · 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 MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 88

Resolution: verified exact; arxiv_id, observed 2026-07-01T06:45:29.683194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.