CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders | Paper Citation Record

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Enhancing diagnosticdeeplearningviaself-supervisedpretraining on large-scale, unlabeled non-medical images.Euro- pean Radiology Experimental, 8(1):10, 2024

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Unresolved cited work

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Understanding intermediate layers using linear classifier probes

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Eyes Wide Shut? Ex- ploringtheVisualShortcomingsofMultimodalLLMs

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This paper cites Frozen Foundation-Model Embeddings Discard Small-Lesion Signal in Chest Radiography: Implications for Pre-Deployment Evaluation.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Frozen Foundation-Model Embeddings Discard Small-Lesion Signal in Chest Radiography: Implications for Pre-Deployment Evaluation

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This paper cites Vision-language models for chest radiography do not always need the image.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Vision-language models for chest radiography do not always need the image

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This paper cites Preva- lence of neural collapse during the terminal phase of deep learning training.Proceedings of the National Academy of Sciences, 117(40):24652–24663, 2020.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Preva- lence of neural collapse during the terminal phase of deep learning training.Proceedings of the National Academy of Sciences, 117(40):24652–24663, 2020

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This paper cites A baseline for detecting misclassified and out-of-distribution exam- ples in neural networks.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders A baseline for detecting misclassified and out-of-distribution exam- ples in neural networks

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Energy-based out-of-distribution detection

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This paper cites A simple unified framework for detecting out- of-distribution samples and adversarial attacks.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders A simple unified framework for detecting out- of-distribution samples and adversarial attacks

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Observation fc112158-5ab4-48ff-89bc-4c83e4b69785 · outbound

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Simple and scalable predictive un- certaintyestimationusingdeepensembles

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Out-of-distribution detection with deep nearest neigh- bors

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Observation 5303ac32-7bea-4c75-bf3e-e86a2149a1b6 · outbound

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Selective clas- sification for deep neural networks

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Position: the platonic representation hy- pothesis

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This paper cites Mat- szangosz, Gergely Papp, and Dániel Varga.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Mat- szangosz, Gergely Papp, and Dániel Varga

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Shortcut learning in deep neural networks.Nature Machine Intelligence, 2 (11):665–673, 2020

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This paper cites Ai for radiographic covid-19 detectionselectsshortcutsoversignal.NatureMachine Intelligence, 3(7):610–619, 2021.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Ai for radiographic covid-19 detectionselectsshortcutsoversignal.NatureMachine Intelligence, 3(7):610–619, 2021

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Observation e23e388e-92fe-46b4-9a7d-c049c101d6c9 · outbound

This paper cites Agentic retrieval-augmented reasoning reshapes col- lective reliability under model variability in radiology question answering, 2026.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Agentic retrieval-augmented reasoning reshapes col- lective reliability under model variability in radiology question answering, 2026

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Observation dc6e2daf-98ea-46f5-9414-711d24ae4700 · outbound

This paper cites The strength of clinical evidence is recoverable from language model representations but not from their stated grades.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders The strength of clinical evidence is recoverable from language model representations but not from their stated grades

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This paper cites Lipschitz-margintraining:Scalablecertificationofper- turbationinvariancefordeepneuralnetworks.NeurIPS 2018, 31, 2018.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Lipschitz-margintraining:Scalablecertificationofper- turbationinvariancefordeepneuralnetworks.NeurIPS 2018, 31, 2018

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Resolution scaling governs dinov3 transferperformanceinchestradiographclassification,

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Mimic- cxr, a de-identified publicly available database of chest radiographswithfree-textreports.Scientificdata,6(1): 317, 2019

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This paper cites Chexpert: A large chest radiograph dataset with un- certainty labels and expert comparison.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Chexpert: A large chest radiograph dataset with un- certainty labels and expert comparison

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Padchest: A large chest x-ray image dataset with multi-label annotated re- ports.MedicalImageAnalysis,66:101797,2020

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Vindr- cxr: An open dataset of chest x-rays with radiologist’s annotations.Scientific Data, 9(1):429, 2022

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Preparing a collection of radiology examinations for distribution and retrieval.Journal of the Ameri- can Medical Informatics Association, 23(2):304–310, 2016

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Augmenting the na- tional institutes of health chest radiograph dataset with expert annotations of possible pneumonia.Radiology: Artificial Intelligence, 1(1):e180041, 2019

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders 100,000histologicalimagesofhumancolorectal cancer and healthy tissue

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders The caltech-ucsd birds- 200-2011 dataset

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Fine-Grained Visual Classification of Aircraft

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Aptos 2019 blind- ness detection

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Feedbackonapubliclydistributedimagedatabase:the messidordatabase.ImageAnalysis&Stereology,pages 231–234, 2014

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Evaluating deep neural networks trained on clinicalimagesindermatologywiththefitzpatrick17k dataset

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Bcn20000: Dermoscopic lesions in the wild.Scien- tific data, 11(1):641, 2024

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Exploring scalable medical image encoders beyond text supervi- sion.NatureMachineIntelligence,7(1):119–130,2025

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Towards a general-purpose foun- dation model for computational pathology.Nature medicine, 30(3):850–862, 2024

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CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Nature medicine, 30(10):2924–2935, 2024

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Observation 866c03d4-138f-43dd-9456-30902ab8b810 · outbound

This paper cites A foundation language- image model of the retina (flair): Encoding expert knowledge in text supervision.Medical Image Anal- ysis, 99:103357, 2025.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders A foundation language- image model of the retina (flair): Encoding expert knowledge in text supervision.Medical Image Anal- ysis, 99:103357, 2025

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-01T15:30:22.635923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d067a56-48ba-4c6c-84b8-7dfc1f3b9824 · outbound

This paper cites Transparent medical image ai via an image–text foundation model grounded in medical lit- erature.Nature medicine, 30(4):1154–1165, 2024.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Transparent medical image ai via an image–text foundation model grounded in medical lit- erature.Nature medicine, 30(4):1154–1165, 2024

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-01T15:30:22.780171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b0a116a-099f-46be-9374-574df7e59126 · outbound

This paper cites A multi- modal vision foundation model for clinical dermatol- ogy.Nature Medicine, 31(8):2691–2702, 2025.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders A multi- modal vision foundation model for clinical dermatol- ogy.Nature Medicine, 31(8):2691–2702, 2025

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-01T15:30:22.883769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71ad959c-8db8-4c3e-8c1d-7ba4a3729be5 · outbound

This paper cites DINOv3.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders DINOv3

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-01T15:30:22.956743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45967ea3-f31b-47fb-88f7-b4d48a1f8fdc · outbound

This paper cites DINOv2: Learning robust visual features with- out supervision.Transactions on Machine Learn- ing Research, 2024.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders DINOv2: Learning robust visual features with- out supervision.Transactions on Machine Learn- ing Research, 2024

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-01T15:30:23.082273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 762ec9c7-2f3d-48ab-a5d9-1335b7986bac · outbound

This paper cites Learningtransferablevisualmodels from natural language supervision.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Learningtransferablevisualmodels from natural language supervision

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-01T15:30:23.146303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6edce691-f699-46d0-a270-ec8d040e27db · outbound

This paper cites Siglip2:Mul- tilingual vision-language encoders with improved se- manticunderstanding,localization,anddensefeatures,.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Siglip2:Mul- tilingual vision-language encoders with improved se- manticunderstanding,localization,anddensefeatures,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-01T15:30:23.242178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d77a995-9eea-40a4-9d76-19e190753d14 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Adam: A Method for Stochastic Optimization

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-01T15:30:23.455074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5efb309-0d13-4c28-be13-2ed6cac7a9f5 · outbound

This paper cites Controlling the false discovery rate: a practical and powerful approach to multiple testing.Journal of the Royal statistical so- ciety:seriesB(Methodological),57(1):289–300,1995.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Controlling the false discovery rate: a practical and powerful approach to multiple testing.Journal of the Royal statistical so- ciety:seriesB(Methodological),57(1):289–300,1995

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-01T15:30:23.544534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6abcee2f-fd87-4d7c-a7f6-a2d35eef9a10 · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-01T15:30:23.330384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18fbb8e6-61f5-4f35-a2aa-da6fa3f44d7b · outbound

This paper cites Resolution scaling governs DINOv3 transfer performance in chest radiograph classification.

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders Resolution scaling governs DINOv3 transfer performance in chest radiograph classification

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-01T15:30:19.622831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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