Paper Citation Record · LEDGER

Efficient MedSAMs: Segment Anything in Medical Images on Laptop

As of 21 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 5 inbound Pith citation observations for arXiv:2412.16085.

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

pith.paper-citation-record.v1
2412.16085 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:50:38.855520Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:54:27.449159Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T19:43:23.297891Z

Reference resolution

37 of 37 outbound references displayed

verified exact0
verified fuzzy30
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fd7a909f-6998-4c77-ba04-92ca1d6f902c · outbound

This paper cites Blinded, randomized trial of sonographer versus AI cardiac function assessment,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Blinded, randomized trial of sonographer versus AI cardiac function assessment,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.285579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0ffd480a-b521-4063-bdce-fae8c6b1ffc8 · outbound

This paper cites Large-scale pancreatic cancer detection via non-contrast ct and deep learning,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Large-scale pancreatic cancer detection via non-contrast ct and deep learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.274832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9bf17a1c-46c8-4dea-903d-73ac6f48d156 · outbound

This paper cites Image segmentation using deep learning: A survey,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Image segmentation using deep learning: A survey,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.263885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f49dfe4e-6be5-403e-b601-1e4a3634c38b · outbound

This paper cites Segment Anything.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Segment Anything

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T10:50:38.725333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76c2bbcd-4699-45de-aef0-84bd431822f8 · outbound

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

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Segment anything model for medical image analysis: an experimental study,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.252950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 31cd7519-8155-4fc8-adac-3ae9bfa7e80d · outbound

This paper cites Segment anything model for medical images?.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Segment anything model for medical images?

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.241434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bc6709a6-a0f8-458f-b694-84a49a40c2d5 · outbound

This paper cites Segment Everything Everywhere All at Once.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Segment Everything Everywhere All at Once

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T10:50:38.744997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a019886-d86d-4705-b06b-6a0091756f87 · outbound

This paper cites Segment anything in medical images,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Segment anything in medical images,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.229212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c223f5d2-daac-42b6-b7f1-36f8c98132ea · outbound

This paper cites A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.217430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bac92ac5-3ed9-4e34-a9de-29a10bedee6b · outbound

This paper cites Deep interactive segmentation of medical images: A systematic review and taxonomy,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Deep interactive segmentation of medical images: A systematic review and taxonomy,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.205145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c683063c-c46a-41fd-81a1-dd15c1f23d07 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Imagenet: A large-scale hierarchical image database,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.193562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b579cd4d-f729-4b01-a1ff-b79c1f1a763e · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Imagenet classification with deep convolutional neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.181953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c129d751-7682-4807-8b09-019444cfc2b7 · outbound

This paper cites Highly accurate protein structure prediction with alphafold,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Highly accurate protein structure prediction with alphafold,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T10:50:38.768437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb1eb7cf-c45b-427c-831c-d7184c607013 · outbound

This paper cites Artificial intelligence for diagnosis and gleason grading of prostate cancer: the panda challenge,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Artificial intelligence for diagnosis and gleason grading of prostate cancer: the panda challenge,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.164814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 43708652-ca84-4703-8a03-4283d083945c · outbound

This paper cites Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the flare22 challenge,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the flare22 challenge,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.154214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6d4951dc-3630-423d-881f-3cb200136718 · outbound

This paper cites Touchstone benchmark: Are we on the right way for evaluating AI algorithms for medical segmentation?.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Touchstone benchmark: Are we on the right way for evaluating AI algorithms for medical segmentation?

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.143851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 10089d56-5d84-4004-b88b-ba23b96da92e · outbound

This paper cites Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.133256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f7d4084f-3188-4000-8445-193d6f3ff7eb · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.122026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2fd66d65-09ff-464f-8cb3-0c2fa56975c3 · outbound

This paper cites Tinyvit: Fast pretraining distillation for small vision transformers,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Tinyvit: Fast pretraining distillation for small vision transformers,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.111374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cb5ca21a-fd7f-435c-ac2d-6e202a225074 · outbound

This paper cites Medficientsam: A robust medical segmentation model with optimized inference pipeline for limited clinical settings,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Medficientsam: A robust medical segmentation model with optimized inference pipeline for limited clinical settings,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.100626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b83b2ffd-b7da-4626-a3fe-711f92e9d0dc · outbound

This paper cites Daft: Data-aware fine-tuning of foundation models for efficient and effective medical image segmentation,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Daft: Data-aware fine-tuning of foundation models for efficient and effective medical image segmentation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.090055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4775270d-85b8-4f8d-913b-7a18cdc24aaf · outbound

This paper cites Rep-medsam: Towards real-time and universal medical image segmentation,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Rep-medsam: Towards real-time and universal medical image segmentation,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.079582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 34dfa88c-b5d9-4f54-8d1a-510d0875b6c6 · outbound

This paper cites Repvit: Revisiting mobile cnn from vit perspective,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Repvit: Revisiting mobile cnn from vit perspective,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.068932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 49672502-e53d-45e3-95ff-d1fae9fa34a0 · outbound

This paper cites Children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.058274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7e3a7cce-a6a0-4d52-b359-5e47a5916c5f · outbound

This paper cites Results from the autopet challenge on fully automated lesion segmentation in oncologic pet/ct imaging,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Results from the autopet challenge on fully automated lesion segmentation in oncologic pet/ct imaging,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.047427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c51cf8c8-0987-46ed-980f-7c79583fe54b · outbound

This paper cites Reproducibility analysis: Reproduce the top one team results,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Reproducibility analysis: Reproduce the top one team results,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.035720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dd2c5d96-c462-4b27-8b64-3a7a89194d17 · outbound

This paper cites Efficientvit: Lightweight multi-scale attention for high-resolution dense prediction,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Efficientvit: Lightweight multi-scale attention for high-resolution dense prediction,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.024347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0497ded8-c5e4-4586-8431-08afed878b79 · outbound

This paper cites 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:39.012153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1a025f92-5c3b-4b94-a0ff-334c8d9288d3 · outbound

This paper cites Abdomenatlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Abdomenatlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:38.999086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c0fac649-54eb-440b-ae64-a06b28b36e7e · outbound

This paper cites A Survey on Trustworthiness in Foundation Models for Medical Image Analysis.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop A Survey on Trustworthiness in Foundation Models for Medical Image Analysis

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T10:50:38.831389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5dc2c7e-9d55-4579-bbc2-12a95b1a7ae6 · outbound

This paper cites Large-Vocabulary Segmentation for Medical Images with Text Prompts.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Large-Vocabulary Segmentation for Medical Images with Text Prompts

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T10:50:38.835680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2894dae0-73a9-4784-b9df-7383357147ac · outbound

This paper cites nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:38.988145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cd53dba4-0667-4869-86c1-b28d37eba44d · outbound

This paper cites Decoupled weight decay regularization,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Decoupled weight decay regularization,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:38.976096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 54026b76-8872-4446-aa88-900fb3e8b447 · outbound

This paper cites Metrics reloaded: recommendations for image analysis validation,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Metrics reloaded: recommendations for image analysis validation,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:38.964827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5be6e1f1-0d26-44c4-b680-5e23482eb04c · outbound

This paper cites Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T10:50:38.851106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 507ae23f-53d1-40de-9a8f-a8aa9995f597 · outbound

This paper cites Methods and open-source toolkit for analyzing and visualizing challenge results,.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop Methods and open-source toolkit for analyzing and visualizing challenge results,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:50:38.953343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1c1f72d0-0041-46e9-a2f3-16059cc8fa2d · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Efficient MedSAMs: Segment Anything in Medical Images on Laptop SAM 2: Segment Anything in Images and Videos

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T10:50:38.733576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 063cd06f-0b0c-48d2-a5a7-f74ccffb7a0c · inbound

On Efficient Variants of Segment Anything Model: A Survey cites this paper.

On Efficient Variants of Segment Anything Model: A Survey Efficient MedSAMs: Segment Anything in Medical Images on Laptop

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:43:23.304243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 64020e8e-dd78-4fdc-973f-d3250100229c · inbound

MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day cites this paper.

MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day Efficient MedSAMs: Segment Anything in Medical Images on Laptop

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T20:16:48.580921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fce6201-ac73-4463-b051-ad18ea9a7a54 · inbound

ENSAM: an efficient foundation model for interactive segmentation of 3D medical images cites this paper.

ENSAM: an efficient foundation model for interactive segmentation of 3D medical images Efficient MedSAMs: Segment Anything in Medical Images on Laptop

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T15:54:27.449159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a54a5849-f252-40b0-becd-39e4628df74e · inbound

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation cites this paper.

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation Efficient MedSAMs: Segment Anything in Medical Images on Laptop

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T21:46:43.208696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8096a0c2-61d1-4260-ad18-196e9f46ef2f · inbound

ReportMedSAM: Guiding Segmentation Through Radiology Reports cites this paper.

ReportMedSAM: Guiding Segmentation Through Radiology Reports Efficient MedSAMs: Segment Anything in Medical Images on Laptop

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T14:39:18.365047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.