Paper Citation Record · LEDGER

MISSFormer: An Effective Medical Image Segmentation Transformer

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 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

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Source: paper_references, paper_reference_links

measured 17 of 17 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 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:17:32.469284Z

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

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 774c4c90-22e2-4c1f-9bb5-c216160f5207 · inbound

Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation cites this paper.

Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T20:14:37.821045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45f9ead0-9467-4475-8c19-e92ba6852be0 · inbound

HResFormer: Hybrid Residual Transformer for Volumetric Medical Image Segmentation cites this paper.

HResFormer: Hybrid Residual Transformer for Volumetric Medical Image Segmentation MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 464

Resolution: unresolved; no resolver link, observed 2026-08-11T14:58:38.261971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b6a0b22-1456-4af7-8163-990b78a34d95 · inbound

Hierarchical LoG Bayesian Neural Network for Enhanced Aorta Segmentation cites this paper.

Hierarchical LoG Bayesian Neural Network for Enhanced Aorta Segmentation MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T19:06:36.466990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eabaf9e0-8e59-4e44-ba35-e59c93fd028c · inbound

Prostate-Specific Foundation Models for Enhanced Detection of Clinically Significant Cancer cites this paper.

Prostate-Specific Foundation Models for Enhanced Detection of Clinically Significant Cancer MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-09T19:21:35.047424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4afb5a4e-d77e-4c8f-8ccb-97384dd5b807 · inbound

UD-Mamba: A pixel-level uncertainty-driven Mamba model for medical image segmentation cites this paper.

UD-Mamba: A pixel-level uncertainty-driven Mamba model for medical image segmentation MISSFormer: An Effective Medical Image Segmentation Transformer

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T13:41:40.317736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+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 2026-08-16T06:30:59.297886+00:00.

Observation 180d15cb-fccb-4011-8165-1c942052e736 · inbound

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

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.