Paper Citation Record · LEDGER

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging

As of 23 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 2 inbound Pith citation observations for arXiv:2411.11939.

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

pith.paper-citation-record.v1
2411.11939 v1

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

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measured 70 of 70 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T08:05:25.406491Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T08:13:15.868783Z

Reference resolution

68 of 68 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b2c175ce-8f03-436d-ace0-fa9e49e05f07 · outbound

This paper cites Model compression.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Model compression

Reference 1

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Observation 1e3911b6-5315-4373-9b78-1bb951a89d5a · outbound

This paper cites Swad: Domain generalization by seeking flat minima.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Swad: Domain generalization by seeking flat minima

Reference 2

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Observation 8bfa4866-2b56-4870-990c-b56439f8a6ab · outbound

This paper cites Ethical machine learning in healthcare.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Ethical machine learning in healthcare

Reference 3

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Observation 265cf5b3-43ee-46b4-939a-ebc47bc21e9e · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 4

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Observation 21959d12-4637-4447-8d97-bb1c3cbf0222 · outbound

This paper cites Algorithmic fairness in artificial in- telligence for medicine and healthcare.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Algorithmic fairness in artificial in- telligence for medicine and healthcare

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.347034Z

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Observation b5a537ba-a874-41ab-9ee7-9234a0e13b1e · outbound

This paper cites Measures of the amount of ecologic association between species.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Measures of the amount of ecologic association between species

Reference 6

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Source-reported events for the cited work

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Observation 2c0df0b4-baf6-480b-8167-123224c6f8ce · outbound

This paper cites FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-12T18:17:17.667448Z

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Observation e816f49f-1a3e-47f6-b4f6-314f3a0f9f9d · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 8

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Observation d72e57ea-6c2c-4351-bd73-ffc82626855c · outbound

This paper cites The use of ranks to avoid the assumption of normality implicit in the analysis of variance.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging The use of ranks to avoid the assumption of normality implicit in the analysis of variance

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.319992Z

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Observation 54796f4a-aa73-451f-b1e5-54df50e716f4 · outbound

This paper cites Physiobank, physiotoolkit, and physionet: compo- nents of a new research resource for complex physiologic signals.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Physiobank, physiotoolkit, and physionet: compo- nents of a new research resource for complex physiologic signals

Reference 10

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Observation 414e5464-eb7a-4962-b7ec-85ccbd4884b0 · outbound

This paper cites Knowledge distillation: A survey.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Knowledge distillation: A survey

Reference 11

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Source-reported events for the cited work

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Observation 85eaeef8-4845-41e9-8abe-87f5464eef0d · outbound

This paper cites Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.282095Z

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Observation 3e48b00d-fe8b-4ab3-8b6d-453c1661db05 · outbound

This paper cites FFB: A fair fairness benchmark for in-processing group fairness methods.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging FFB: A fair fairness benchmark for in-processing group fairness methods

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.264051Z

Source-reported events for the cited work

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Observation 1d465e99-6e7b-4c7c-8e31-0b04e32feb38 · outbound

This paper cites Deep residual learning for image recognition.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Deep residual learning for image recognition

Reference 14

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Observation 1ac40f6d-b71c-4f8f-95a9-2e16265565a6 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Distilling the Knowledge in a Neural Network

Reference 15

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Observation eca1b9a0-fea2-4b3f-9ce3-108441ed10b2 · outbound

This paper cites Simple data balancing achieves com- petitive worst-group-accuracy.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Simple data balancing achieves com- petitive worst-group-accuracy

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.240467Z

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Observation 64efe83e-04ba-4b09-9da8-9d0c54ffaee1 · outbound

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

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison

Reference 17

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Observation 3d25b97a-1505-4cb0-afe3-555a42bddd69 · outbound

This paper cites ´Etude comparative de la distribution florale dans une portion des alpes et des jura.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging ´Etude comparative de la distribution florale dans une portion des alpes et des jura

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.218686Z

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Observation 5985e707-2cc3-4930-a97d-09e386feb5f4 · outbound

This paper cites MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs

Reference 19

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Observation c1e447a9-c578-4f1d-a6aa-de40f8397cf0 · outbound

This paper cites Achieving fairness in medical devices.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Achieving fairness in medical devices

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.197048Z

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Observation c8f1a24f-9c51-4eb4-a7f0-9771584fd011 · outbound

This paper cites Learning not to learn: Training deep neural networks with biased data.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Learning not to learn: Training deep neural networks with biased data

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.185488Z

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Observation 8f809230-4516-43c4-98c3-513aab01f0a6 · outbound

This paper cites Multi- accuracy: Black-box post-processing for fairness in classifi- cation.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Multi- accuracy: Black-box post-processing for fairness in classifi- cation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.172406Z

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Observation 4e18a26f-41f6-40a4-b71d-d47cd5acad6c · outbound

This paper cites Papila: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Papila: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment

Reference 23

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Observation aeeb5676-10f9-45c9-bf98-8d1e764e5f89 · outbound

This paper cites Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.148705Z

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Observation f21fc0a9-d592-4514-8378-bf340361c4a2 · outbound

This paper cites Asymmetric temper- ature scaling makes larger networks teach well again.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Asymmetric temper- ature scaling makes larger networks teach well again

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.135666Z

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Observation 1ae639e6-394a-485e-a9e7-e6dc5793e08c · outbound

This paper cites Bias mitigation post-processing for individual and group fairness.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Bias mitigation post-processing for individual and group fairness

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.123742Z

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Observation 8ed5f063-1b56-4008-ad74-e128d7479832 · outbound

This paper cites Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks

Reference 27

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Observation 84a957ee-5246-4ec5-8bb6-ccdba89050ce · outbound

This paper cites Harvard glaucoma detection and progression: A multimodal multitask dataset and generalization-reinforced semi-supervised learning.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Harvard glaucoma detection and progression: A multimodal multitask dataset and generalization-reinforced semi-supervised learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.103672Z

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Observation a6046e2a-4887-401f-b608-3b7ec3ba1608 · outbound

This paper cites FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging FairVision: Equitable Deep Learning for Eye Disease Screening via Fair Identity Scaling

Reference 29

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Observation 098b1f8b-124b-4c45-bdc6-81ed32cfe58c · outbound

This paper cites Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization

Reference 30

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Observation 6c71e368-d171-4a67-af03-e6316188ce64 · outbound

This paper cites Fairvi- sion: Equitable deep learning for eye disease screening via fair identity scaling, 2024.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Fairvi- sion: Equitable deep learning for eye disease screening via fair identity scaling, 2024

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.091446Z

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Observation 2f68755e-b7cc-4325-9087-1d496368515a · outbound

This paper cites Fairclip: Har- nessing fairness in vision-language learning.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Fairclip: Har- nessing fairness in vision-language learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.079043Z

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Observation 33ea3876-1fee-43b3-88e9-a82a7e1782bd · outbound

This paper cites Learning adversarially fair and transferable represen- tations.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Learning adversarially fair and transferable represen- tations

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.066695Z

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Observation c728ce76-ed93-407c-8301-33d167aeaf37 · outbound

This paper cites Systematic outperformance of 112 der- matologists in multiclass skin cancer image classification by convolutional neural networks.European Journal of Cancer, 119:57–65, 2019.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Systematic outperformance of 112 der- matologists in multiclass skin cancer image classification by convolutional neural networks.European Journal of Cancer, 119:57–65, 2019

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.054404Z

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Observation 5b4e9f01-dd05-446a-a426-5768632b6564 · outbound

This paper cites Minimax pareto fairness: A multi objective perspective.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Minimax pareto fairness: A multi objective perspective

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.041742Z

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Observation 4e969bf6-063f-42b2-8e9c-7bf5104c90e4 · outbound

This paper cites Whinston, Jerry R.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Whinston, Jerry R

Reference 36

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Observation 4c2f7aa1-c9b0-4295-b9f9-be0d8ff532ea · outbound

This paper cites A survey on bias and fairness in machine learning.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging A survey on bias and fairness in machine learning

Reference 37

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This paper cites Distribution-free multiple compar- isons.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Distribution-free multiple compar- isons

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:18.009215Z

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Observation d4fb79df-9c3c-4d53-a186-706b0166f2a3 · outbound

This paper cites Smote: synthetic minority over-sampling technique.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Smote: synthetic minority over-sampling technique

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.997475Z

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Observation 361f654c-5ec6-467a-8acc-da658be7a025 · outbound

This paper cites Dissecting racial bias in an algorithm used to manage the health of populations.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Dissecting racial bias in an algorithm used to manage the health of populations

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.985873Z

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Observation 87f99fdb-f9a2-4982-9fc0-81900bcc959e · outbound

This paper cites Fair contrastive learn- ing for facial attribute classification.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Fair contrastive learn- ing for facial attribute classification

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.974573Z

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Observation e8b0b910-0bc9-4b91-b066-a5b122c4f095 · outbound

This paper cites On fairness and calibration.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging On fairness and calibration

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.962900Z

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Observation 27fe1a8b-36c3-4904-9ebf-625c1626018b · outbound

This paper cites Discovering fair representations in the data domain.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Discovering fair representations in the data domain

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.951271Z

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Observation 23ef9c70-5a36-435e-8044-e95e68f42d90 · outbound

This paper cites Fair attribute classification through latent space de-biasing.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Fair attribute classification through latent space de-biasing

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.937678Z

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Observation a5236b72-3705-41e3-9d82-dfbdcd3be85e · outbound

This paper cites Fr-train: A mutual information-based approach to fair and robust training.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Fr-train: A mutual information-based approach to fair and robust training

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.924449Z

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Observation ba70c045-cd44-4864-8b94-80e986196d7c · outbound

This paper cites Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Reference 46

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Observation 2b33ca90-6ec4-4dd9-96fb-cbf90bd56e49 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 47

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This paper cites Fairness by learning orthogonal disentan- gled representations.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Fairness by learning orthogonal disentan- gled representations

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.911369Z

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Observation afb6db50-3356-4c22-a34b-7da94e106039 · outbound

This paper cites Equitable ar- tificial intelligence for glaucoma screening with fair identity normalization.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Equitable ar- tificial intelligence for glaucoma screening with fair identity normalization

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.896697Z

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Observation e54c7f6a-0c11-41f8-8696-3773cf0f3458 · outbound

This paper cites Equitable deep learning for diabetic retinopathy detection using multi-dimensional reti- nal imaging with fair adaptive scaling: a retrospective study.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Equitable deep learning for diabetic retinopathy detection using multi-dimensional reti- nal imaging with fair adaptive scaling: a retrospective study

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.883910Z

Source-reported events for the cited work

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Observation b32e8311-be28-4062-96db-0bb0a60af2dd · outbound

This paper cites A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegeta- tion on danish commons.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegeta- tion on danish commons

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.868696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 446e1428-0022-4306-8cc7-c36e3a25ffb9 · outbound

This paper cites End: Entangling and disentangling deep rep- resentations for bias correction.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging End: Entangling and disentangling deep rep- resentations for bias correction

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.857330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation aa03e666-0279-4dbe-bf8e-d6ba10e944b4 · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.844500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f5b9c261-92ee-4620-aa20-108f0991b68c · outbound

This paper cites Fairseg: A large-scale medical image seg- mentation dataset for fairness learning using segment any- thing model with fair error-bound scaling.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Fairseg: A large-scale medical image seg- mentation dataset for fairness learning using segment any- thing model with fair error-bound scaling

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.832136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation aee13c0e-3a42-457e-9159-44875168881b · outbound

This paper cites FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-12T18:17:17.446912Z

Source-reported events for the cited work

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Observation 33668a06-556b-4be2-904d-fc2399669c73 · outbound

This paper cites The ham10000 dataset, a large collection of multi-source der- matoscopic images of common pigmented skin lesions.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging The ham10000 dataset, a large collection of multi-source der- matoscopic images of common pigmented skin lesions

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.820241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f7644fe0-c19a-446c-9594-6b377ad0b968 · outbound

This paper cites An overview of statistical learning the- ory.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging An overview of statistical learning the- ory

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T18:17:17.454366Z

Source-reported events for the cited work

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Observation 2196de5b-15b7-48db-a4d6-01abf30c2425 · outbound

This paper cites Fairness-aware ad- versarial perturbation towards bias mitigation for deployed deep models.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Fairness-aware ad- versarial perturbation towards bias mitigation for deployed deep models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.801599Z

Source-reported events for the cited work

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Observation 6495c496-2d72-4f07-801b-19e41869e8e4 · outbound

This paper cites Cutmix: Regu- larization strategy to train strong classifiers with localizable features.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Cutmix: Regu- larization strategy to train strong classifiers with localizable features

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.789060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 55c647f1-a5ba-4630-b75f-2cb6812d16d2 · outbound

This paper cites Fairness constraints: Mechanisms for fair classification.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Fairness constraints: Mechanisms for fair classification

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.775230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f2bb85fd-2130-4134-bb8d-3f3eceb2fa78 · outbound

This paper cites Mitigating unwanted biases with adversarial learning.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Mitigating unwanted biases with adversarial learning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.760249Z

Source-reported events for the cited work

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Observation b2b71b24-76d5-4815-8183-08b61e0c9892 · outbound

This paper cites Towards accuracy-fairness para- dox: Adversarial example-based data augmentation for vi- sual debiasing.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Towards accuracy-fairness para- dox: Adversarial example-based data augmentation for vi- sual debiasing

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.743568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1f3466d2-0527-4494-84ff-204aa12d4d59 · outbound

This paper cites Conditional Learning of Fair Representations.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging Conditional Learning of Fair Representations

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T18:17:17.478134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ce5b6ec-0d83-4832-8506-665027981d29 · outbound

This paper cites MEDFAIR: Benchmarking Fairness for Medical Imaging.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging MEDFAIR: Benchmarking Fairness for Medical Imaging

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T18:17:17.482863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70453d07-2e35-47c4-8317-f2d45be11895 · outbound

This paper cites For data splitting.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging For data splitting

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.728935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3a9a64e2-d55e-43a4-8c70-05931180ad3b · outbound

This paper cites All datasets are publicly available and can be accessed through the URLs listed in Table 4.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging All datasets are publicly available and can be accessed through the URLs listed in Table 4

Reference 66

Resolution: verified exact; doi, observed 2026-08-12T18:17:17.717650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 071f3cdf-03df-46c9-84f3-ca70bcb9ae42 · outbound

This paper cites This measure promotes fairness by proportionally reducing the overall AUC in response to increased subgroup disparities.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging This measure promotes fairness by proportionally reducing the overall AUC in response to increased subgroup disparities

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.704635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0efafc96-c7b0-4d34-9074-174442866965 · outbound

This paper cites 11 shows the complete set of classification results, while Tabs.

Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging 11 shows the complete set of classification results, while Tabs

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:17:17.691917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 20430451-373e-45fe-bdd8-8956242eaa03 · inbound

People-Centred Medical Image Analysis via Fairness-Aware Human-AI Cooperation cites this paper.

People-Centred Medical Image Analysis via Fairness-Aware Human-AI Cooperation Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging

Reference 91

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:36:39.799659Z

Source-reported events for the cited work

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Observation c9f7905c-1e94-4a4a-897e-6875a3359d95 · inbound

Fairness Beyond Demographics: Optimizing Performance Across Appearance-Based Hidden Cohorts in Medical Imaging cites this paper.

Fairness Beyond Demographics: Optimizing Performance Across Appearance-Based Hidden Cohorts in Medical Imaging Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T08:13:15.870122Z

Source-reported events for the cited work

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