Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-14T12:21:48.661331Z
Paper Citation Record · LEDGER
As of 27 July 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.10358.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-14T12:21:48.661331Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-07-27T06:30:09.085275+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
35 of 35 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 2c5cf1dc-c031-422b-8cd6-13c2b84dbab9 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Data6(11), 111 (2021)
Reference 1
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Observation b0c396d5-e14d-49f5-93c8-289b346f483d · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Unresolved cited work
Reference 2
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Observation 4326bb5c-484b-4c08-957f-af299d6d7502 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Data in Brief55, 110633 (2024)
Reference 3
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Observation b893d5d3-b5f1-40f4-a574-6c555a6c853a · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift On the Opportunities and Risks of Foundation Models
Reference 4
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Observation 632ce889-35d6-4aef-a2d4-8f0754fae36d · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift JAMA Network Open4(8), e2119100 (2021)
Reference 5
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Observation 7ec0941a-3c91-4255-a7a7-02f2519f4411 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Scientific Data10(1), 123 (2023)
Reference 6
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Observation 559d9aa3-1e37-476f-a2cd-16aac6ae2506 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in Mammography
Reference 7
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Observation 9e07652e-8bea-4fcd-9d95-d4acebe86c34 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift GLAM: Geometry-Guided Local Alignment for Multi-View VLP in Mammography
Reference 8
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Observation 29bf5e24-1723-47f7-8753-a5e85ccfa454 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Bias and Generalizability of Foundation Models across Datasets in Breast Mammography
Reference 9
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Observation bbfad378-b5af-4dca-a30c-ba43651a742a · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, pp
Reference 10
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Observation 629786d5-c152-4769-a778-d0f28372c71f · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Mammo-FM: Breast-specific foundational model for Integrated Mammographic Diagnosis, Prognosis, and Reporting
Reference 11
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Observation 4052fd9e-0b20-4538-b55b-d62229113e1d · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Radiology: Artificial Intelligence5(6), e230060 (2023)
Reference 12
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Observation 90f9b5ba-1722-4f01-a9f3-ababf956a5cc · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Radiology: Artificial Intelligence3(1), e200103 (2021)
Reference 13
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Observation 1d8a72bb-ca41-4ac1-ad95-5f5b659c6f92 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp
Reference 14
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Observation 77f84011-8469-4218-8fdd-5aa03e29f3e4 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift arXiv preprint arXiv:2509.20271 (2025)
Reference 15
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Observation 458909af-930b-4d3a-8e21-428b9e1e5bc1 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Radiology: Artificial Intelligence5(1), e220047 (2023)
Reference 16
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Observation b501c151-12f7-429b-b6cb-05530da1f96e · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Scientific Data12(1), 1479 (2025)
Reference 17
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Observation 4874232b-cb77-4d74-bb81-60c01b32c1f0 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Scientific Data 9(1), 122 (2022)
Reference 18
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Observation 7e347c4c-9dfc-4f77-a00b-67031b1b0737 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities
Reference 19
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Observation 4db2a01a-475d-4c77-afd8-24fdc0fd7782 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Scientific Data 4(1), 170177 (2017) 10
Reference 20
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Observation 63cc40d0-b262-4340-817d-029cb79f95f7 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift European Radiology Experimental5(1), 40 (2021)
Reference 21
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Observation 23b31f97-a073-453a-9c55-07bc9688e57d · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Reference 22
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Observation e9076dfd-1fa0-4b07-b5d9-5e5f9acb9d86 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Nature616, 259–265 (2023)
Reference 23
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Observation eeb89741-38ba-4b97-8147-76027561f250 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Academic Radiology19(2), 236–248 (2012)
Reference 24
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Observation 3dea6379-2f56-49e8-83b5-8b96db4a8ddc · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning
Reference 25
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Observation 511ad54d-d15b-4408-b374-b82f660a5402 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Scientific Data10(1), 277 (2023)
Reference 26
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Observation f0420e6f-9c17-4147-bd80-28e35bc0f5b8 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift DINOv2: Learning Robust Visual Features without Supervision
Reference 27
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Observation 2218a3f0-0d0b-41f4-b8d4-758a34e50ef6 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Biomedical Engineering Letters14(2), 317–330 (2024)
Reference 28
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Observation 606d42de-0379-4c71-b6a7-a5293001877d · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Radiology314(2), e240597 (2025)
Reference 29
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Observation 72f32b20-855d-400b-b6e3-2d141d6fb2c4 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Nature Machine Intelligence7(1), 119–130 (2025)
Reference 30
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Observation 0bce7578-8a9e-4bb9-a74c-1d1c7c4add05 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Unresolved cited work
Reference 31
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Observation 9607c92f-9a3d-4434-99ad-66a5c5a723ce · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift MedGemma Technical Report
Reference 32
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Observation 633d476e-8f40-4eef-a456-e8c563525d67 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift DINOv3
Reference 33
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Observation c9e83000-55dc-4b27-8001-24b89505982c · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift Apollo - University of Cambridge Repository (2015)
Reference 34
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Observation b64dd8ed-f64b-4468-a381-6199f7649830 · outbound
Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs
Reference 35
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No inbound Pith citation observations are available.