Paper Citation Record · LEDGER

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially

As of 10 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 2 inbound Pith citation observations for arXiv:2502.01000.

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

pith.paper-citation-record.v1
2502.01000 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:01:12.157787Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:01:12.037600Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T18:52:35.631268Z

Reference resolution

27 of 27 outbound references displayed

verified exact1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 19ef11e2-9b48-4dd7-bb3a-81b769ca3838 · outbound

This paper cites Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially

Reference 1

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This paper cites an unresolved cited work.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Unresolved cited work

Reference 2

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Observation d54e858b-f5aa-409f-88d5-62f6895f98e5 · outbound

This paper cites an unresolved cited work.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Unresolved cited work

Reference 3

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This paper cites an unresolved cited work.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Unresolved cited work

Reference 4

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Observation ad26dda1-1555-4475-ba56-3f0dcf1d0a43 · outbound

This paper cites Ethical approval was not required as confirmed by the license attached with the open access data.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Ethical approval was not required as confirmed by the license attached with the open access data

Reference 5

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Observation 30019b48-0a7d-4476-ac7c-521a2328ab94 · outbound

This paper cites Progressive transfer learning and adversarial do- main adaptation for cross-domain skin disease classifi- cation,.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Progressive transfer learning and adversarial do- main adaptation for cross-domain skin disease classifi- cation,

Reference 6

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Observation edceb90f-cd33-4c41-8fb8-6248936edf94 · outbound

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

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Swin- unet: Unet-like pure transformer for medical image seg- mentation,

Reference 7

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Observation 7d00c18e-73bc-457f-81a3-9112adbf6444 · outbound

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

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation

Reference 8

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Observation a5aa000d-ccc0-49cb-a09b-b929d85b3e14 · outbound

This paper cites MONAI: An open-source framework for deep learning in healthcare.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially MONAI: An open-source framework for deep learning in healthcare

Reference 9

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Observation a8d7c35c-96c9-409c-9460-d4571e73dd73 · outbound

This paper cites A domain-adaptive u- net for electron microscopy image segmentation,.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially A domain-adaptive u- net for electron microscopy image segmentation,

Reference 10

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Observation 569781a7-2ecd-4f80-ac18-12bf43b164ca · outbound

This paper cites Unsupervised domain adaptation with variational approximation for cardiac segmentation,.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Unsupervised domain adaptation with variational approximation for cardiac segmentation,

Reference 11

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Observation 9240aa0c-0cee-42db-bae4-bbead0f8b831 · outbound

This paper cites Ban- dit problems and the exploration/exploitation tradeoff,.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Ban- dit problems and the exploration/exploitation tradeoff,

Reference 12

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Observation faabe367-d8c7-43fd-95f5-25357cf28735 · outbound

This paper cites (5) This allows us to balance the exploitation of arms with a high predicted reward and the exploration of areas with high un- certainty.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially (5) This allows us to balance the exploitation of arms with a high predicted reward and the exploration of areas with high un- certainty

Reference 13

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Observation 6ad0777a-ae94-4059-b5dc-7baf647a1b01 · outbound

This paper cites Auxiliary Learning by Implicit Differentiation.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Auxiliary Learning by Implicit Differentiation

Reference 14

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Observation b584f794-92fd-478d-82a5-2b3c36b5a090 · outbound

This paper cites One model is all you need: multi-task learning enables simultaneous histology image segmen- tation and classification,.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially One model is all you need: multi-task learning enables simultaneous histology image segmen- tation and classification,

Reference 15

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Observation f7c94af6-aef5-49c3-9996-697987087d5d · outbound

This paper cites Improving few-shot generalization by exploring and exploiting auxiliary data,.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Improving few-shot generalization by exploring and exploiting auxiliary data,

Reference 16

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Observation b029b18a-3592-411e-9741-60b9e3335978 · outbound

This paper cites Joint pvl detection and manual ability classification using semi-supervised multi-task learning,.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Joint pvl detection and manual ability classification using semi-supervised multi-task learning,

Reference 17

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Observation 6241fb92-12c4-4d39-a813-60dbadec49f6 · outbound

This paper cites Gradient surgery for multi-task learning,.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Gradient surgery for multi-task learning,

Reference 18

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Observation 7127be64-24ae-473f-b00d-53d200f1993f · outbound

This paper cites Finite-time analysis of the multiarmed bandit prob- lem,.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Finite-time analysis of the multiarmed bandit prob- lem,

Reference 19

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Observation 2eaff6fa-3aee-4c97-9572-7bc643e3f68c · outbound

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

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially U-net: Convolutional networks for biomedical im- age segmentation,

Reference 20

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Observation d9ca1e48-4cab-46d6-86ea-b346bc02d85c · outbound

This paper cites Flemme: A flexible and modular learning platform for medical images,.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Flemme: A flexible and modular learning platform for medical images,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:01:12.345391Z

Source-reported events for the cited work

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Observation 26f2e218-093f-41d2-a3e1-ef1cd12a383b · outbound

This paper cites Nonstationary Stochastic Multiarmed Bandits: UCB Policies and Minimax Regret.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Nonstationary Stochastic Multiarmed Bandits: UCB Policies and Minimax Regret

Reference 22

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Observation 3d1cc6d0-afeb-46f0-b6e5-cf50e26dff51 · outbound

This paper cites The Federated Tumor Segmentation (FeTS) Challenge.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially The Federated Tumor Segmentation (FeTS) Challenge

Reference 23

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Observation 1e1eba43-453b-403c-80ba-3af65ba2d86c · outbound

This paper cites Multi-site infant brain seg- mentation algorithms: The iseg-2019 challenge,.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Multi-site infant brain seg- mentation algorithms: The iseg-2019 challenge,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:01:12.330081Z

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Observation da6b41c9-cbbc-4d6c-99f4-cb525ca3a4d9 · outbound

This paper cites Standardized assessment of au- tomatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge,.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Standardized assessment of au- tomatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:01:12.314780Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4d3a2015-5673-442c-b345-077a42ba7982 · outbound

This paper cites Totalsegmentator: robust segmentation of 104 anatomic structures in ct images,.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially Totalsegmentator: robust segmentation of 104 anatomic structures in ct images,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:01:12.300363Z

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Observation c0cf2337-4d98-4843-9512-207cef6ac370 · outbound

This paper cites The medical seg- mentation decathlon,.

Adapting Foundation Models for Few-Shot Medical Image Segmentation: Actively and Sequentially The medical seg- mentation decathlon,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T17:01:12.285727Z

Source-reported events for the cited work

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Pith citing papers

Observation 19ef11e2-9b48-4dd7-bb3a-81b769ca3838 · inbound