Paper Citation Record · LEDGER

SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2311.11969.

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

pith.paper-citation-record.v1
2311.11969 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 20 of 20 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:23:03.250848Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T13:16:58.492036Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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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 53169a36-9c93-4476-a02a-e31960efaaa1 · inbound

GMAI-VL & GMAI-VL-5.5M: A Large Vision-Language Model and A Comprehensive Multimodal Dataset Towards General Medical AI cites this paper.

GMAI-VL & GMAI-VL-5.5M: A Large Vision-Language Model and A Comprehensive Multimodal Dataset Towards General Medical AI SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-12T15:16:27.923422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53db8e16-eb6d-4288-95be-dd88775e7499 · inbound

Intuitive Axial Augmentation Using Polar-Sine-Based Piecewise Distortion for Medical Slice-Wise Segmentation cites this paper.

Intuitive Axial Augmentation Using Polar-Sine-Based Piecewise Distortion for Medical Slice-Wise Segmentation SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T22:35:56.174095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6372037-d3ff-4f12-b2f1-527d8eb77914 · inbound

MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance cites this paper.

MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 136

Resolution: unresolved; no resolver link, observed 2026-08-11T11:43:08.866507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4df41cd2-d9da-4d39-9485-c4850eebf467 · inbound

ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical Images cites this paper.

ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical Images SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-08T17:26:28.869297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 618e0598-1a05-44a0-bdcb-53efd32ae0b9 · inbound

RadSAM: Segmenting 3D radiological images with a 2D promptable model cites this paper.

RadSAM: Segmenting 3D radiological images with a 2D promptable model SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T05:23:03.250848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cd17bf8-1674-47c7-a927-d5e8d029cc81 · inbound

The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review cites this paper.

The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-15T22:50:53.567207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59b97ce0-4b37-4576-85f6-5cf9708e2ac6 · inbound

Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaboration cites this paper.

Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaboration SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-15T22:35:46.118221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41777000-a87b-4474-b38f-f7d239bfbd03 · inbound

DrVD-Bench: Do Vision-Language Models Reason Like Human Doctors in Medical Image Diagnosis? cites this paper.

DrVD-Bench: Do Vision-Language Models Reason Like Human Doctors in Medical Image Diagnosis? SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T12:35:12.802983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d14e06ab-eaef-419c-837d-9192b775c05c · 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 SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 165

Resolution: unresolved; no resolver link, observed 2026-08-15T20:08:51.568194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88e08575-e7bc-483b-b4d6-4bd5119e7578 · inbound

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus cites this paper.

SafeClick: Error-Tolerant Interactive Segmentation of Any Medical Volumes via Hierarchical Expert Consensus SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:37.817418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 409a9a2e-1a8b-4723-a891-dd3e30ae5463 · inbound

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation cites this paper.

MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T17:34:47.357364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89c26356-62ea-4ccd-af91-f6f6c4525371 · inbound

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images cites this paper.

MediRound: Multi-Round Entity-Level Reasoning Segmentation in Medical Images SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-03T22:08:38.537491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58c97126-eddf-46c9-9569-f8b15d2f7123 · inbound

Beyond Medical Diagnostics: How Medical Multimodal Large Language Models Think in Space cites this paper.

Beyond Medical Diagnostics: How Medical Multimodal Large Language Models Think in Space SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T18:16:34.075552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd1d7cbf-f72f-4ac3-a0ee-b0fde5f4839c · inbound

Spatial navigation in preclinical Alzheimer's disease: A review cites this paper.

Spatial navigation in preclinical Alzheimer's disease: A review SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 98

Resolution: unresolved; no resolver link, observed 2026-07-13T19:53:53.219607Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad8e4595-c596-4bda-b4f2-89f9f07b1383 · inbound

MedVeriSeg: Teaching MLLM-Based Medical Segmentation Models to Verify Query Validity Without Extra Training cites this paper.

MedVeriSeg: Teaching MLLM-Based Medical Segmentation Models to Verify Query Validity Without Extra Training SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:26:02.055090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4eadfe25-8d95-4c52-b0e8-cbf3d7de99af · inbound

From Boundaries to Semantics: Prompt-Guided Multi-Task Learning for Petrographic Thin-section Segmentation cites this paper.

From Boundaries to Semantics: Prompt-Guided Multi-Task Learning for Petrographic Thin-section Segmentation SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:00:03.852896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0e38be63-c7b5-47bc-be97-804ce4099213 · inbound

Med-R2: An Adversarial Benchmark for Evidence-Grounded Reasoning in Medical VLMs cites this paper.

Med-R2: An Adversarial Benchmark for Evidence-Grounded Reasoning in Medical VLMs SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T14:04:44.350657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9becd34f-1195-483e-b8d3-c35cecb99f24 · inbound

MedSIGHT: Towards Grounded Visual Comprehension in Medical Large Vision-Language Models cites this paper.

MedSIGHT: Towards Grounded Visual Comprehension in Medical Large Vision-Language Models SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-02T13:16:58.493869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ef50c7cd-a954-4a08-81d0-01d43909fd6a · inbound

Dual-Adaptive SAM3: Hierarchical Routing over Low-Rank Expert Layers for Parameter-Efficient Medical Image Segmentation cites this paper.

Dual-Adaptive SAM3: Hierarchical Routing over Low-Rank Expert Layers for Parameter-Efficient Medical Image Segmentation SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-12T10:17:15.944505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 371fd059-7488-4e7f-94be-3911a5d59320 · inbound

Location-Aware Fine-Grained Representation Learning for Medical Vision Foundation Models cites this paper.

Location-Aware Fine-Grained Representation Learning for Medical Vision Foundation Models SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T00:40:24.496366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.