Paper Citation Record · LEDGER

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation

As of 12 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2501.09138.

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

pith.paper-citation-record.v1
2501.09138 v1

Coverage vector

measured 46 of 46 reference resolution

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

Source: arxiv_reference, observed 2026-05-22T15:34:57.779679Z

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation f445f971-43df-400f-b32c-0d6daecf1c9c · outbound

This paper cites Segment anything in medical images,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Segment anything in medical images,

Reference 1

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Observation 6d457990-397b-4fb1-9970-50deee1eb48d · outbound

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

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Medical image segmentation using deep learning: A survey,

Reference 2

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Observation 63786cfa-48ef-4410-ba57-8bc930693e93 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Learning transferable visual models from natural language supervision,

Reference 3

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Observation 450cc084-1ae4-4080-9010-d417eae11452 · outbound

This paper cites Segment anything,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Segment anything,

Reference 4

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Observation 8d4a83e2-9927-4eb4-a320-bd28a7d56097 · outbound

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

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation SAM 2: Segment Anything in Images and Videos

Reference 5

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Observation d75d76f9-3a72-4797-9ad8-bb8cec3f0dab · outbound

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

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Segment anything model for medical image analysis: an experimental study,

Reference 6

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Observation 06f1fb29-17da-45f6-b00a-e04b0a87ff60 · outbound

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

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Segment anything model for medical image segmentation: Current applications and future directions,

Reference 7

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Observation d524ce52-a242-43cc-824c-39155fa712a0 · outbound

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

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images

Reference 8

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Observation 7fc8ce1e-e28d-4bd6-a33d-4bf673088399 · outbound

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

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

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Observation 1d4a4005-ecfb-42b9-adac-c79c349a983c · outbound

This paper cites Emerging properties in self-supervised vision transformers,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Emerging properties in self-supervised vision transformers,

Reference 10

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Observation 40a759dd-261d-46ac-8c14-6a0f330676fa · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation DINOv2: Learning Robust Visual Features without Supervision

Reference 11

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Observation b6efe707-216e-4c25-95ab-5cc98bd30087 · outbound

This paper cites Denoising diffusion probabilistic models,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Denoising diffusion probabilistic models,

Reference 12

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Observation a4ab7d16-9948-4a40-947d-d398aa7a4468 · outbound

This paper cites Deep ViT Features as Dense Visual Descriptors.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Deep ViT Features as Dense Visual Descriptors

Reference 13

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Observation a1321857-e0d0-4eef-85de-c9ec8ae86640 · outbound

This paper cites Localizing Objects with Self-Supervised Transformers and no Labels.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Localizing Objects with Self-Supervised Transformers and no Labels

Reference 14

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Observation 56b01f1c-5310-4082-936c-be7adea7d67e · outbound

This paper cites Sclip: Rethinking self-attention for dense vision-language inference,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Sclip: Rethinking self-attention for dense vision-language inference,

Reference 15

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Observation ad2d4eef-881b-4f8c-b690-6d6a8ff989e1 · outbound

This paper cites Probabilistic prompt learning for dense prediction,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Probabilistic prompt learning for dense prediction,

Reference 16

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Observation ad296c59-df0e-4cca-bf14-a0cf62f41813 · outbound

This paper cites Language-driven visual consensus for zero-shot semantic segmenta- tion,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Language-driven visual consensus for zero-shot semantic segmenta- tion,

Reference 17

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Observation 50232f90-e213-46d5-86a7-d2223ed2d5d9 · outbound

This paper cites Generative semantic segmenta- tion,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Generative semantic segmenta- tion,

Reference 18

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Observation ebf2669a-7a40-4303-8a8e-b42fc613197d · outbound

This paper cites Diffuse attend and segment: Unsupervised zero-shot segmentation using stable diffusion,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Diffuse attend and segment: Unsupervised zero-shot segmentation using stable diffusion,

Reference 19

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Observation 598ca9c7-9da3-4f95-aa1e-9f636373a1f9 · outbound

This paper cites Masked-attention mask transformer for universal image segmentation,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Masked-attention mask transformer for universal image segmentation,

Reference 20

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This paper cites Oneformer: One transformer to rule universal image segmentation,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Oneformer: One transformer to rule universal image segmentation,

Reference 21

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Observation e2b9327e-58b6-44be-8568-236d09aa0c72 · outbound

This paper cites SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model

Reference 22

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Observation 12071307-05cf-44cd-9db1-54fd5b1d6d6d · outbound

This paper cites The segment anything foundation model achieves favorable brain tumor auto-segmentation accuracy in mri to support radiotherapy treatment planning,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation The segment anything foundation model achieves favorable brain tumor auto-segmentation accuracy in mri to support radiotherapy treatment planning,

Reference 23

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Observation cecda38d-0bb4-49e6-89ad-be9d0a2f6804 · outbound

This paper cites Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Reference 24

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This paper cites Sam meets robotic surgery: an empirical study on generalization, robustness and adaptation,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Sam meets robotic surgery: an empirical study on generalization, robustness and adaptation,

Reference 25

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Observation 21049c7b-ff66-4ec9-98e9-e7b04c3a106b · outbound

This paper cites Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey

Reference 26

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Observation 7bd5408c-1c1a-4d57-80e3-bc2e6b967b71 · outbound

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

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Customized Segment Anything Model for Medical Image Segmentation

Reference 27

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This paper cites Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Cheap Lunch for Medical Image Segmentation by Fine-tuning SAM on Few Exemplars

Reference 28

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Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation SAM-Med2D

Reference 29

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Observation dbfc6fd7-dda7-4e2f-8877-e274180fd392 · outbound

This paper cites Hiera: A hierarchi- cal vision transformer without the bells-and-whistles,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Hiera: A hierarchi- cal vision transformer without the bells-and-whistles,

Reference 30

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This paper cites Masked au- toencoders are scalable vision learners,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Masked au- toencoders are scalable vision learners,

Reference 31

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This paper cites Modern information retrieval: A brief overview,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Modern information retrieval: A brief overview,

Reference 32

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

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Medical SAM 2: Segment medical images as video via Segment Anything Model 2

Reference 33

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This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 34

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Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation U-net: Convolutional networks for biomedical image segmentation,

Reference 35

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

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Unetr: Transformers for 3d medical image segmentation,

Reference 36

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Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images,

Reference 37

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This paper cites Segmentation of knee images: a grand challenge,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Segmentation of knee images: a grand challenge,

Reference 38

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Observation 512a0496-88ee-4e62-9717-f4f12989f8e0 · outbound

This paper cites Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?

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Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Miccai multi-atlas labeling beyond the cranial vault–workshop and challenge,

Reference 40

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Observation 27041fe9-b916-4416-896c-e4abaf313309 · outbound

This paper cites Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features,

Reference 41

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This paper cites The medical segmentation decathlon,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation The medical segmentation decathlon,

Reference 42

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Observation e45e730c-9473-4b4e-8b6f-decd317bc33e · outbound

This paper cites Mean squared error: Love it or leave it? a new look at signal fidelity measures,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Mean squared error: Love it or leave it? a new look at signal fidelity measures,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:15:27.008688Z

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Observation 95c0edd9-595f-41ff-9d8e-af73692b0f29 · outbound

This paper cites Image matching by normalized cross- correlation,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Image matching by normalized cross- correlation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:15:26.991988Z

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Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Unresolved cited work

Reference 45

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Observation 9c47613b-f359-4086-8206-baa4176b837f · outbound

This paper cites Pearson correlation coefficient,.

Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation Pearson correlation coefficient,

Reference 46

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Pith citing papers

Observation 13a47842-886c-4260-8e2d-f284746695cb · inbound

A Survey on Foundation Models for Personalized Federated Intelligence cites this paper.

A Survey on Foundation Models for Personalized Federated Intelligence Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical Image Segmentation

Reference 17

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