Paper Citation Record · LEDGER

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer

As of 12 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2412.13908.

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

pith.paper-citation-record.v1
2412.13908 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:43:39.815036Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

19 of 19 outbound references displayed

verified exact1
verified fuzzy11
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 39faf0de-ac74-4d42-9831-9c69080c0dd5 · outbound

This paper cites State-of-the-art methods for brain tissue segmentation: A review,.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer State-of-the-art methods for brain tissue segmentation: A review,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:43:40.484188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 611f6b31-4ff6-4786-8c79-b3a3dfb8a592 · outbound

This paper cites A survey on deep learning for skin lesion segmentation,.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer A survey on deep learning for skin lesion segmentation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:43:40.469034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1102763f-8aab-4bfb-bde0-18c38418da67 · outbound

This paper cites Swin transformer improves the idh mutation status prediction of gliomas free of mri-based tumor segmentation,.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer Swin transformer improves the idh mutation status prediction of gliomas free of mri-based tumor segmentation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:43:40.452979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation eb5c5c80-3c44-41ec-b0d9-16524f79a3cf · outbound

This paper cites Deep learning techniques for tumor segmentation: a review,.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer Deep learning techniques for tumor segmentation: a review,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:43:40.437352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 70455dce-6916-46df-b687-8c4daf006384 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmen- tation,.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer U-net: Convolutional networks for biomedical image segmen- tation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:43:40.420606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a967aa67-e5b2-4c4d-995b-92979843e355 · outbound

This paper cites Clusterseg: A crowd cluster pinpointed nucleus segmentation framework with cross-modality datasets,.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer Clusterseg: A crowd cluster pinpointed nucleus segmentation framework with cross-modality datasets,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:43:40.143694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 95dac1e3-a476-437b-bb17-3f489da46a71 · outbound

This paper cites Transnuseg: A lightweight multi-task transformer for nuclei segmen- tation,.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer Transnuseg: A lightweight multi-task transformer for nuclei segmen- tation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:43:40.116524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b118bfbe-e1b2-4b63-b4a4-3618536a6754 · outbound

This paper cites MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T12:43:39.510980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3c3f431-581b-4945-b902-bbf89bcab302 · outbound

This paper cites Segment Anything.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer Segment Anything

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T12:43:39.516776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f51d4af-0029-4041-b3af-981179737bdb · outbound

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

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T12:43:39.522038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 676a7382-3d4c-4d07-9d6b-63c9c4f5a4ff · outbound

This paper cites Segment anything in medical images,.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer Segment anything in medical images,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:43:40.099143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4b29b807-abf6-4aa5-8c93-d374ed1b4b1a · outbound

This paper cites SAM-Med2D.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer SAM-Med2D

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T12:43:39.532450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b848a6f7-bcad-423a-be81-7a799717f612 · outbound

This paper cites Segment anything model for medical image analysis: an experimental study,.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer Segment anything model for medical image analysis: an experimental study,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:43:40.083473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e744ba17-fc4c-4131-b5c4-9965d610d026 · outbound

This paper cites SAM3D: Segment Anything Model in Volumetric Medical Images.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer SAM3D: Segment Anything Model in Volumetric Medical Images

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T12:43:39.542591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03563e7c-841f-4306-a7a9-5dfdc7148fd8 · outbound

This paper cites SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T12:43:39.547552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22254337-c85d-4d6c-9d5b-b7dc441493c8 · outbound

This paper cites FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-11T12:43:39.936945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0fc3f02c-6cc7-4219-b7d6-696abfb62e56 · outbound

This paper cites Memorizing Transformers.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer Memorizing Transformers

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T12:43:39.632856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68efbee0-ca5f-4fc0-bdcb-6c25b266596d · outbound

This paper cites Movit: Memorizing vision transformers for medical image analysis,.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer Movit: Memorizing vision transformers for medical image analysis,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:43:40.066812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c6c027d3-d693-41b9-ad77-fc59796e2c59 · outbound

This paper cites Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images,.

Memorizing SAM: 3D Medical Segment Anything Model with Memorizing Transformer Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:43:40.051624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.