Paper Citation Record · LEDGER

MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2306.16925.

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

pith.paper-citation-record.v1
2306.16925 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:08:50.876916Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T05:25:54.556865Z

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 d838540c-1c2a-48f3-823e-51cf3ffde519 · inbound

MG-3D: Multi-Grained Knowledge-Enhanced 3D Medical Vision-Language Pre-training cites this paper.

MG-3D: Multi-Grained Knowledge-Enhanced 3D Medical Vision-Language Pre-training MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T20:20:59.834892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2128f54f-f8a5-4c38-b0a1-84ab01cab81d · inbound

An OpenMind for 3D medical vision self-supervised learning cites this paper.

An OpenMind for 3D medical vision self-supervised learning MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T05:53:40.825810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ef51c41-b02e-4281-81b6-8e77d5ad8042 · inbound

Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research cites this paper.

Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

Reference 143

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:50.876916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98c458b1-90e9-4f00-be54-61bde74c2cbc · inbound

Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models cites this paper.

Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T18:29:08.761161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 507d4fe7-f3de-4cc5-85a4-c047bbdb3bcd · inbound

TinyUSFM: Towards Compact and Efficient Ultrasound Foundation Models cites this paper.

TinyUSFM: Towards Compact and Efficient Ultrasound Foundation Models MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:25:54.559045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8dccaa57-91c2-4886-98e6-43b13ade2c0f · inbound

CORA: Generalizable coronary artery disease assessment and risk stratification from coronary CT angiography using pathology-centric representation learning cites this paper.

CORA: Generalizable coronary artery disease assessment and risk stratification from coronary CT angiography using pathology-centric representation learning MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

Reference 40

Resolution: unresolved; no resolver link, observed 2026-07-13T18:35:27.619230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d04633bc-a7df-4357-9c42-fffc7090129e · inbound

Uncertainty-Aware Foundation Models for Clinical Data cites this paper.

Uncertainty-Aware Foundation Models for Clinical Data MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-13T16:48:03.001740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 840adb31-61f7-48b0-b628-e2bfe8ee91fd · inbound

Expanders Meet Reed-Muller: Easy Instances of Noisy k-XOR cites this paper.

Expanders Meet Reed-Muller: Easy Instances of Noisy k-XOR MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

Reference 62

Resolution: unresolved; no resolver link, observed 2026-07-13T11:03:20.369425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7811ce4-1ea0-46af-a523-77929724fa54 · inbound

Knowledge Transfer Scaling Laws for 3D Medical Imaging cites this paper.

Knowledge Transfer Scaling Laws for 3D Medical Imaging MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

Reference 37

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T01:45:51.614077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 827384b8-594c-49bf-bfbc-17cd244f9875 · inbound

MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms cites this paper.

MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset

Reference 100

Resolution: unresolved; no resolver link, observed 2026-07-14T16:31:09.661787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.