Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-14T09:21:21.442535Z
Paper Citation Record · LEDGER
As of 14 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2607.10777.
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 2026-07-14T09:21:21.442535Z
One-hop event checks from named stored sources.
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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.
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60 of 60 outbound references displayed
External citation measurements
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Observation 498868e0-2a8c-4092-9422-2e5ad74d7166 · outbound
RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Distribution matching losses can hallucinate features in medical image translation
Reference 1
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Association of biomarker-based artificial intelligence with risk of racial bias in retinal images.JAMA ophthalmology, 141(6):543–552, 2023
Reference 3
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Arcface: Additive angular margin loss for deep face recognition.IEEE Trans
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization An image is worth 16x16 words: Trans- formers for image recognition at scale
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Observation 282f8f52-5a19-499b-ac27-896486772aa5 · outbound
RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Tsaftaris, and Timothy M
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Reference 9
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Observation f806608f-db6d-4126-840e-12f3d27900b8 · outbound
RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Ai recognition of patient race in medical imaging: a modelling study.The Lancet Digital Health, 4(6):e406–e414, 2022
Reference 10
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Observation 33d11cc9-4d89-4a73-9265-33861c4228f0 · outbound
RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Algorith- mic encoding of protected characteristics in chest x-ray disease detection models
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization In search of lost domain generalization
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Equality of opportunity in super- vised learning
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Mambavision: A hybrid mamba-transformer vision backbone
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Deep residual learning for image recognition
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization doi: 10.1109/CVPR.2017.243
Reference 20
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Re- visiting masked image modeling with standardized color space for domain generalized fundus photography classification
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Reference 22
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Kusner, Joshua R
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Let samples speak: Mitigating spurious correlation by exploiting the clusterness of samples
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Reference 27
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Incomplete modality disentangled representa- tion for ophthalmic disease grading and diagnosis
Reference 28
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Sphereface revived: Unifying hyperspherical face recognition.IEEE Trans
Reference 29
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Swin transformer: Hierarchical vision transformer using shifted windows
Reference 30
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Fairvision: Equitable deep learning for eye disease screening via fair identity scaling, 2024
Reference 33
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Fairclip: Harnessing fairness in vision-language learning
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Shufflenet V2: prac- tical guidelines for efficient CNN architecture design
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Slicing through bias: Explaining performance gaps in medical image analysis using slice discovery meth- ods
Reference 38
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Observation 94e5bdc8-dde7-486e-ba02-55d4e919b4ed · outbound
RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Prevalence of Neural Collapse during the terminal phase of deep learning training
Reference 39
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Reference 40
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Machine learning derived retinal pigment score from ophthalmic imaging shows ethnicity is not biology.Nature communications, 16(1):60, 2025
Reference 41
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
Reference 42
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RED-Sphere: Hyperspherical Residual Edge Debiasing for Cross-Population Fundus Disease Domain Generalization The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets.PloS one, 10 (3):e0118432, 2015
Reference 43
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Reference 45
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Reference 46
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Reference 48
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Reference 50
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Observation 1d9b31ee-0db3-46d8-a925-73d486b624f7 · outbound
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Reference 51
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Reference 52
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Reference 55
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Reference 56
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Reference 57
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Observation fca47f77-ea84-4886-bc20-f5b43b2339b4 · outbound
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Reference 58
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Reference 60
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