Paper Citation Record · LEDGER

Diffusion-empowered AutoPrompt MedSAM

As of 12 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 3 inbound Pith citation observations for arXiv:2502.06817.

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

pith.paper-citation-record.v1
2502.06817 v2

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:56:36.007532Z

measured 52 of 52 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:56:03.602547Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T21:58:19.882256Z

Reference resolution

49 of 49 outbound references displayed

verified exact3
verified fuzzy29
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f6c14f8c-81e6-42ba-b769-e05feece9cd6 · outbound

This paper cites Medical image segmentation review: The success of u- net,.

Diffusion-empowered AutoPrompt MedSAM Medical image segmentation review: The success of u- net,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:37.000220Z

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 d7391eb5-c1d8-4d68-8af5-650ca401e752 · outbound

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

Diffusion-empowered AutoPrompt MedSAM Deep interactive segmentation of medical images: A systematic review and taxonomy,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.979430Z

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 cb29de0d-d9e7-48ad-813e-5767402a736f · outbound

This paper cites Robustly optimized deep feature decoupling network for fatty liver diseases detection,.

Diffusion-empowered AutoPrompt MedSAM Robustly optimized deep feature decoupling network for fatty liver diseases detection,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.959444Z

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 e8b866aa-6351-43d6-9fd3-417b1dc795ee · outbound

This paper cites Artificial intelligence in image-based cardio- vascular disease analysis: A comprehensive survey and future outlook,.

Diffusion-empowered AutoPrompt MedSAM Artificial intelligence in image-based cardio- vascular disease analysis: A comprehensive survey and future outlook,

Reference 4

Resolution: verified exact; raw_fallback, observed 2026-08-09T10:56:36.393822Z

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 b02ad2f6-4b28-4aca-b687-7a04ef6dfa9e · outbound

This paper cites Unetr: Transformers for 3d medical image segmentation,.

Diffusion-empowered AutoPrompt MedSAM Unetr: Transformers for 3d medical image segmentation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.936933Z

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 e4decc22-fb0d-4917-9b4c-222f4bbb69df · outbound

This paper cites U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation.

Diffusion-empowered AutoPrompt MedSAM U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.778770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 822a138e-9a33-497d-a57d-80d73c274c7e · outbound

This paper cites nnformer: V olumetric medical image segmentation via a 3d transformer,.

Diffusion-empowered AutoPrompt MedSAM nnformer: V olumetric medical image segmentation via a 3d transformer,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.920197Z

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 5dfbee34-971b-4568-ae1f-8f3433766c20 · outbound

This paper cites A survey of transfer learning,.

Diffusion-empowered AutoPrompt MedSAM A survey of transfer learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.902100Z

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 d0c28f8a-96d0-4d3e-8aef-166ac919a04e · outbound

This paper cites Transfer learning: a friendly introduction,.

Diffusion-empowered AutoPrompt MedSAM Transfer learning: a friendly introduction,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.884938Z

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 e05e90bd-c315-4bdf-b83d-bcefbaeed295 · outbound

This paper cites Segment anything,.

Diffusion-empowered AutoPrompt MedSAM Segment anything,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.868140Z

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 171d2d81-0c12-45d9-bd94-4dc17e32486e · outbound

This paper cites A Comprehensive Survey on Segment Anything Model for Vision and Beyond.

Diffusion-empowered AutoPrompt MedSAM A Comprehensive Survey on Segment Anything Model for Vision and Beyond

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.805048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45f4e19b-f61c-46ae-b370-b57ddfd2f6c1 · outbound

This paper cites MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM.

Diffusion-empowered AutoPrompt MedSAM MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.811166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c26ddba-8a0b-44cd-9edf-768117569a36 · outbound

This paper cites Segment anything in medical images,.

Diffusion-empowered AutoPrompt MedSAM Segment anything in medical images,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.817567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb3deffc-88d2-4bea-80c8-1ed0268aac7e · outbound

This paper cites AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation.

Diffusion-empowered AutoPrompt MedSAM AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-09T10:56:36.258431Z

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 fdf215b8-825d-4f95-8897-646c23beb83c · outbound

This paper cites Surgicalsam: Efficient class promptable surgical instrument segmentation,.

Diffusion-empowered AutoPrompt MedSAM Surgicalsam: Efficient class promptable surgical instrument segmentation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.840144Z

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 7495065c-e02c-4e6b-b019-8891d181b82e · outbound

This paper cites MaskSAM: Towards Auto-prompt SAM with Mask Classification for Volumetric Medical Image Segmentation.

Diffusion-empowered AutoPrompt MedSAM MaskSAM: Towards Auto-prompt SAM with Mask Classification for Volumetric Medical Image Segmentation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.833577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12f54cd9-6cfe-4073-ac4f-f4d3f19c3bc9 · outbound

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

Diffusion-empowered AutoPrompt MedSAM Segment anything model for medical image analysis: An experimental study,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.820252Z

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 d5848c34-9a31-483d-844a-c6d40e051bff · outbound

This paper cites Segment anything model for medical image segmentation: Current applications and future directions,.

Diffusion-empowered AutoPrompt MedSAM Segment anything model for medical image segmentation: Current applications and future directions,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.843919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42538be8-f57e-41e0-9330-5bd14ab70345 · outbound

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

Diffusion-empowered AutoPrompt MedSAM Medficientsam: A robust medical segmentation model with optimized inference pipeline for limited clinical settings,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.787795Z

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 74759e52-1e3e-4bc2-b4e8-69a60bc35cfa · outbound

This paper cites SAM-Med2D.

Diffusion-empowered AutoPrompt MedSAM SAM-Med2D

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.854247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02dd1a38-31f3-4ddf-9032-8822741b2298 · outbound

This paper cites Uv-sam: Adapting segment anything model for urban village identification,.

Diffusion-empowered AutoPrompt MedSAM Uv-sam: Adapting segment anything model for urban village identification,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.769809Z

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 d77192e1-c80f-451e-a64c-67a0eaa94acb · outbound

This paper cites Adaptivesam: Towards efficient tuning of sam for surgical scene segmentation,.

Diffusion-empowered AutoPrompt MedSAM Adaptivesam: Towards efficient tuning of sam for surgical scene segmentation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.750749Z

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 0d57302d-f6de-4bbc-9053-1a9412ce4deb · outbound

This paper cites SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation.

Diffusion-empowered AutoPrompt MedSAM SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.869254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7435f03c-da99-4513-b011-0a69e1f9d0de · outbound

This paper cites Cold segdiffusion: A novel diffusion model for medical image segmentation,.

Diffusion-empowered AutoPrompt MedSAM Cold segdiffusion: A novel diffusion model for medical image segmentation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.731931Z

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 4c8c543f-5bef-448f-8314-e2466a3caad1 · outbound

This paper cites A diffusion model multi-scale feature fusion network for imbalanced medical image clas- sification research,.

Diffusion-empowered AutoPrompt MedSAM A diffusion model multi-scale feature fusion network for imbalanced medical image clas- sification research,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.714640Z

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 a40602cd-184c-4cff-857f-b2d883b748f7 · outbound

This paper cites Dif- fusion models for counterfactual generation and anomaly detection in brain images,.

Diffusion-empowered AutoPrompt MedSAM Dif- fusion models for counterfactual generation and anomaly detection in brain images,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.696142Z

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 ded5e30b-e724-4dfe-93d5-e15d9eaf2684 · outbound

This paper cites Diffusion models in medical imaging: A compre- hensive survey,.

Diffusion-empowered AutoPrompt MedSAM Diffusion models in medical imaging: A compre- hensive survey,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.679252Z

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 f78aa9fb-c835-4f84-b460-6a9a7fa41646 · outbound

This paper cites Brain tumor segmentation using synthetic mr images-a comparison of gans and diffusion models,.

Diffusion-empowered AutoPrompt MedSAM Brain tumor segmentation using synthetic mr images-a comparison of gans and diffusion models,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.896303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57129fa4-8302-4a01-907e-a278bea9bd91 · outbound

This paper cites Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms.

Diffusion-empowered AutoPrompt MedSAM Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-09T10:56:36.173085Z

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 86a69986-656e-4556-9a88-72cf5595eb8b · outbound

This paper cites Target-guided diffusion models for unpaired cross- modality medical image translation,.

Diffusion-empowered AutoPrompt MedSAM Target-guided diffusion models for unpaired cross- modality medical image translation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.652188Z

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 ad72624b-bc69-4327-919f-3e0714fe6c4e · outbound

This paper cites Synthetic ct generation from mri using 3d transformer- based denoising diffusion model,.

Diffusion-empowered AutoPrompt MedSAM Synthetic ct generation from mri using 3d transformer- based denoising diffusion model,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.635314Z

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 4d62fb1b-cced-4441-aeed-43fce0c1c618 · outbound

This paper cites Dce-diff: Diffusion model for synthesis of early and late dynamic contrast-enhanced mr images from non- contrast multimodal inputs,.

Diffusion-empowered AutoPrompt MedSAM Dce-diff: Diffusion model for synthesis of early and late dynamic contrast-enhanced mr images from non- contrast multimodal inputs,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.618386Z

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 23999f27-9691-42d6-93ac-0bbded142a56 · outbound

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

Diffusion-empowered AutoPrompt MedSAM U-net: Convolutional networks for biomedical image segmentation,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.922130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2dd6fb3f-d87e-450a-ad7c-da151ff5b40c · outbound

This paper cites Wider or deeper: Revisiting the resnet model for visual recognition,.

Diffusion-empowered AutoPrompt MedSAM Wider or deeper: Revisiting the resnet model for visual recognition,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.589883Z

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 3bdd96f8-9592-4db6-b72f-d2b44372910f · outbound

This paper cites Uu- mamba: Uncertainty-aware u-mamba for cardiac image segmentation,.

Diffusion-empowered AutoPrompt MedSAM Uu- mamba: Uncertainty-aware u-mamba for cardiac image segmentation,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.570966Z

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 4818503b-693b-4176-ab1a-e0d1799b385c · outbound

This paper cites Dual-term loss function for shape- aware medical image segmentation,.

Diffusion-empowered AutoPrompt MedSAM Dual-term loss function for shape- aware medical image segmentation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.553221Z

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 50b6f51a-2fbe-4535-95f1-2d5e23337537 · outbound

This paper cites Abdomenct-1k: Is abdominal organ segmentation a solved problem?.

Diffusion-empowered AutoPrompt MedSAM Abdomenct-1k: Is abdominal organ segmentation a solved problem?

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.531660Z

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 aa54717f-f245-4b47-ac8b-99241be8f3ae · outbound

This paper cites The multimodal brain tumor image segmentation benchmark (brats),.

Diffusion-empowered AutoPrompt MedSAM The multimodal brain tumor image segmentation benchmark (brats),

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.507310Z

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 5526049b-a13c-4bd4-9cfd-b85a2df718f7 · outbound

This paper cites Kvasir-seg: A segmented polyp dataset,.

Diffusion-empowered AutoPrompt MedSAM Kvasir-seg: A segmented polyp dataset,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.488869Z

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 4f2148d1-4dbf-4e34-9a2f-f838fc41e07b · outbound

This paper cites Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration,.

Diffusion-empowered AutoPrompt MedSAM Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.460838Z

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 caf5297d-c4bb-4bf1-a6dc-3622a3fa27c6 · outbound

This paper cites AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation.

Diffusion-empowered AutoPrompt MedSAM AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.963343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4021b16f-75fe-4b0c-bba6-994c45a658e8 · outbound

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

Diffusion-empowered AutoPrompt MedSAM SAM 2: Segment Anything in Images and Videos

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.968377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e691855-7b7e-443a-a8dd-f11186a1ceb4 · outbound

This paper cites Challenge Summary U-MedSAM: Uncertainty-aware MedSAM for Medical Image Segmentation.

Diffusion-empowered AutoPrompt MedSAM Challenge Summary U-MedSAM: Uncertainty-aware MedSAM for Medical Image Segmentation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.973549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 041ea48f-ef3c-458c-877c-8b5c4eed1db1 · outbound

This paper cites Customized Segment Anything Model for Medical Image Segmentation.

Diffusion-empowered AutoPrompt MedSAM Customized Segment Anything Model for Medical Image Segmentation

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.979664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81e35246-74e7-42c2-b259-73028a54311d · outbound

This paper cites Unleashing the potential of sam for medical adaptation via hierarchical decoding,.

Diffusion-empowered AutoPrompt MedSAM Unleashing the potential of sam for medical adaptation via hierarchical decoding,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.440970Z

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 5a51840b-d117-44d8-a4da-6f69705838e4 · outbound

This paper cites How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images.

Diffusion-empowered AutoPrompt MedSAM How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.991666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdd5201d-d521-4386-ae21-b58d4b240a89 · outbound

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

Diffusion-empowered AutoPrompt MedSAM nnu-net: a self-configuring method for deep learning-based biomedical image segmentation,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:35.997105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34444b0c-31cc-4622-b8bd-fc07ec2c1cf3 · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmentation,.

Diffusion-empowered AutoPrompt MedSAM Swin-unet: Unet-like pure transformer for medical image segmentation,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:56:36.411533Z

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 6e7f76b5-cc8f-4518-b7ed-4ab2f8bc89aa · outbound

This paper cites A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark.

Diffusion-empowered AutoPrompt MedSAM A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-09T10:56:36.007532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation a04d7fa8-bb61-4ed6-8f56-1259d326a78a · inbound

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges cites this paper.

Prompt Engineering in Segment Anything Model: Methodologies, Applications, and Emerging Challenges Diffusion-empowered AutoPrompt MedSAM

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:03.602547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 295f0557-1043-420e-866b-eff010e05ad0 · inbound

Robust Multi-Source Covid-19 Detection in CT Images cites this paper.

Robust Multi-Source Covid-19 Detection in CT Images Diffusion-empowered AutoPrompt MedSAM

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-13T21:58:19.883676Z

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 95b92adf-9a33-4c36-8c46-55e597480524 · inbound

Weight Group-wise Post-Training Quantization for Medical Foundation Model cites this paper.

Weight Group-wise Post-Training Quantization for Medical Foundation Model Diffusion-empowered AutoPrompt MedSAM

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:11:01.880794Z

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.