Paper Citation Record · LEDGER

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

As of 14 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2412.05888.

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

pith.paper-citation-record.v1
2412.05888 v3

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:16:48.630489Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

14 of 14 outbound references displayed

verified exact0
verified fuzzy3
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0ae47b6b-be8f-4751-9ce3-84f06a090f91 · outbound

This paper cites Medical SAM 2: Segment medical images as video via Segment Anything Model 2.

MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day Medical SAM 2: Segment medical images as video via Segment Anything Model 2

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T20:16:48.630489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea0e080f-3aff-4fc5-8ea6-e45229723100 · outbound

This paper cites Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?.

MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T20:16:48.565097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

This paper cites Efficient MedSAMs: Segment Anything in Medical Images on Laptop.

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 34931d6a-cd72-4ea3-bb5e-2d021dfa6b04 · outbound

This paper cites Radiology objects in context (roco): a multimodal image dataset.

MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day Radiology objects in context (roco): a multimodal image dataset

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:16:48.923810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3f3c9ecd-308d-4a55-ab70-e2e584f31aa4 · outbound

This paper cites RepViT-SAM: Towards Real-Time Segmenting Anything.

MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day RepViT-SAM: Towards Real-Time Segmenting Anything

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T20:16:48.601389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e5e32fc-1e1e-4e1e-aefa-b55a9a244e59 · outbound

This paper cites Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation.

MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T20:16:48.608451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cdb6d23-d13c-4da1-b706-b5380ff98bb0 · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-11T20:16:48.614664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5573010a-156f-4553-971a-c2cf5c84bbd7 · outbound

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

MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day Large-Vocabulary Segmentation for Medical Images with Text Prompts

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T20:16:48.623929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4254d919-bd25-4d62-be90-7461980368e2 · outbound

This paper cites VM-UNet: Vision Mamba UNet for Medical Image Segmentation.

MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day VM-UNet: Vision Mamba UNet for Medical Image Segmentation

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-11T20:16:48.596377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e6132d8-b428-451a-9430-0f8e270a878d · outbound

This paper cites SAM on Medical Images: A Comprehensive Study on Three Prompt Modes.

MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day SAM on Medical Images: A Comprehensive Study on Three Prompt Modes

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-11T20:16:48.557815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93773bd4-47fe-48cc-9b7e-07ecb1f092d5 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-11T20:16:48.552954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d44588b-939f-4689-815d-99d270aea10a · outbound

This paper cites Medfi- cientsam: a robust medical segmentation model with 149 Lyu, Gao and Staring, 2025 optimized inference pipeline for limited clinical settings.

MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day Medfi- cientsam: a robust medical segmentation model with 149 Lyu, Gao and Staring, 2025 optimized inference pipeline for limited clinical settings

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:16:48.939042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b7e34e04-0695-413c-a927-2a15c0047ced · outbound

This paper cites LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation.

MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T20:16:48.575376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfc6eb7b-9ca0-4d57-bd94-05fd0464798f · outbound

This paper cites U- Net: Convolutional networks for biomedical image seg- mentation.

MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day U- Net: Convolutional networks for biomedical image seg- mentation

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:16:48.903543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.