Paper Citation Record · LEDGER

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift

As of 8 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2509.09397.

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

pith.paper-citation-record.v1
2509.09397 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:13:53.999580Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

28 of 28 outbound references displayed

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Outbound references

Observation d2ef3b6a-bbb3-46a2-a619-8ec3e8ba497c · outbound

This paper cites Which clinical decisions benefit from automation? a task complexity approach.International journal of medical infor- matics, 70(2-3):309–316, 2003.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Which clinical decisions benefit from automation? a task complexity approach.International journal of medical infor- matics, 70(2-3):309–316, 2003

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:52.571134Z

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Observation 94b96d06-7d0d-4ca0-89a1-67d4ba2bd9c3 · outbound

This paper cites Mrishift: Disentangled repre- sentation learning for 3d mri lesion segmentation under distributional shifts.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Mrishift: Disentangled repre- sentation learning for 3d mri lesion segmentation under distributional shifts

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:52.627993Z

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Observation e1220fc0-d259-44a8-91df-fe1424f7864f · outbound

This paper cites Heterogeneity in systematic reviews of medical imaging diagnos- tic test accuracy studies: a systematic review.JAMA Network Open, 7(2):e240649– e240649, 2024.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Heterogeneity in systematic reviews of medical imaging diagnos- tic test accuracy studies: a systematic review.JAMA Network Open, 7(2):e240649– e240649, 2024

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:52.664986Z

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Observation be0e2605-dfe6-478e-9e5c-32ddf3de6809 · outbound

This paper cites Reducing reliance on spurious features in medical image clas- sification with spatial specificity.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Reducing reliance on spurious features in medical image clas- sification with spatial specificity

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:52.718487Z

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Observation 2a5b3f60-7283-4ada-a1da-7d7eaca1cb1a · outbound

This paper cites When more is less: Incorporating additional datasets can hurt performance by introducing spuri- ous correlations.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift When more is less: Incorporating additional datasets can hurt performance by introducing spuri- ous correlations

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:52.775748Z

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Observation 22a22c7f-e8ee-4e65-a1a9-04b165ea2982 · outbound

This paper cites Maple: Multi-modal prompt learning.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Maple: Multi-modal prompt learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:52.820745Z

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Observation f17e0dc3-165c-4b21-bca4-bc5e20aa1112 · outbound

This paper cites Conditional prompt learning for vision-language models.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Conditional prompt learning for vision-language models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:52.908830Z

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Observation cab37c37-4854-4111-a92b-b79ae20163e1 · outbound

This paper cites an unresolved cited work.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:52.937052Z

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Observation 3ac69a89-2b67-4ddc-87df-f7039fc74251 · outbound

This paper cites DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift DiMPLe -- Disentangled Multi-Modal Prompt Learning: Enhancing Out-Of-Distribution Alignment with Invariant and Spurious Feature Separation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:52.989408Z

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Observation 4a9ce373-a1b8-4edb-870e-730266f1b871 · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.069878Z

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Observation ba47d356-c6b4-4432-8844-38e8a2f1617f · outbound

This paper cites Test-Time Low Rank Adaptation via Confidence Maximization for Zero-Shot Generalization of Vision-Language Models.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Test-Time Low Rank Adaptation via Confidence Maximization for Zero-Shot Generalization of Vision-Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.096712Z

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Observation 04d31e52-84f6-4345-8d39-d0f23ecdcba7 · outbound

This paper cites A compre- hensive and easy-to-use multi-domain multi-task medical imaging meta-dataset (medimeta).arXiv preprint arXiv:2404.16000, 2024.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift A compre- hensive and easy-to-use multi-domain multi-task medical imaging meta-dataset (medimeta).arXiv preprint arXiv:2404.16000, 2024

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.158548Z

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Observation c628535b-bfbe-4ecb-859d-319f2aa6cfb1 · outbound

This paper cites Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.212106Z

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Observation 39726994-4cd7-458e-b778-73ec0bf94460 · outbound

This paper cites Domain generalization on medical imaging classification us- ing episodic training with task augmentation.Computers in biology and medicine, 141:105144, 2022.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Domain generalization on medical imaging classification us- ing episodic training with task augmentation.Computers in biology and medicine, 141:105144, 2022

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.255050Z

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Observation cef7add1-cf50-4abc-a47e-1020a1edf5f9 · outbound

This paper cites Domain generalization for medical image analysis: A review.Proceedings of the IEEE, 2024.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Domain generalization for medical image analysis: A review.Proceedings of the IEEE, 2024

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.325265Z

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Observation 75eeebd3-1868-4175-b3ab-050d9403b5f8 · outbound

This paper cites Evaluation of do- main generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine.Scientific reports, 12(1):2726, 2022.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Evaluation of do- main generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine.Scientific reports, 12(1):2726, 2022

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.391545Z

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Observation de57c975-87d4-4139-8636-3ffb3fee07af · outbound

This paper cites Few-shot adaptation of medical vision-language models.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Few-shot adaptation of medical vision-language models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.445730Z

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Observation a125acd7-5904-43d5-a980-32f63418ba42 · outbound

This paper cites Clipath: Fine-tune clip with visual feature fusion for pathology image analysis towards minimizing data collection efforts.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Clipath: Fine-tune clip with visual feature fusion for pathology image analysis towards minimizing data collection efforts

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.506124Z

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Observation 75b0ab1b-a219-4e23-b106-cf629ad64b23 · outbound

This paper cites Noise is an Efficient Learner for Zero-Shot Vision-Language Models.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Noise is an Efficient Learner for Zero-Shot Vision-Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.548210Z

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Observation 679573e5-d99e-4fb4-b9fd-524c6bab16ae · outbound

This paper cites Can language-guided unsupervised adaptation improve medical image classification using unpaired images and texts?.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Can language-guided unsupervised adaptation improve medical image classification using unpaired images and texts?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.616300Z

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Observation b1c41123-ea04-4c22-8683-14ed050814c6 · outbound

This paper cites Learning disentangled representations in the imaging domain.Medical Image Analysis, 80:102516, 2022.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Learning disentangled representations in the imaging domain.Medical Image Analysis, 80:102516, 2022

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.680069Z

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Observation a2c998d6-3e7c-4a55-b6ca-655d63b7dbfd · outbound

This paper cites Domain adaptation and generalization of func- tional medical data: A systematic survey of brain data.ACM Computing Surveys, 56(10):1–39, 2024.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Domain adaptation and generalization of func- tional medical data: A systematic survey of brain data.ACM Computing Surveys, 56(10):1–39, 2024

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.703004Z

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Observation d155842b-f9c6-4e2e-a984-66af8963b8ad · outbound

This paper cites Shenzhen hospital chest x-ray (cxr) set.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Shenzhen hospital chest x-ray (cxr) set

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.783153Z

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Observation e9932e46-75ce-46e7-a75a-44bedbb0d046 · outbound

This paper cites Montgomery county chest x-ray (cxr) set.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Montgomery county chest x-ray (cxr) set

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.834724Z

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Observation 3e991736-34ca-4520-89e7-52b56c2d3554 · outbound

This paper cites Indian diabetic retinopathy image dataset (idrid): a database for diabetic retinopathy screening research.Data, 3(3):25, 2018.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Indian diabetic retinopathy image dataset (idrid): a database for diabetic retinopathy screening research.Data, 3(3):25, 2018

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.851286Z

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Observation 4d17bacb-01f6-4d8b-8db9-192ac34ae85c · outbound

This paper cites Isic archive: International skin imaging collaboration dataset.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Isic archive: International skin imaging collaboration dataset

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.895236Z

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Observation e131d344-9f9e-46f7-9fe1-dff80d68d595 · outbound

This paper cites On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable?.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable?

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.947203Z

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Observation 934cb6e0-dfa0-4025-8f0c-af7ee47b0b31 · outbound

This paper cites Amend to alignment: Decoupled prompt tuning for mitigating spuri- ous correlation in vision-language models.

Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift Amend to alignment: Decoupled prompt tuning for mitigating spuri- ous correlation in vision-language models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T19:13:53.999580Z

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Pith citing papers

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