Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging | Paper Citation Record

Observation 977ccc53-af42-467a-af23-c7499ca66b13 · outbound

This paper cites InterpretableCNN-multilevelattentiontransformerforrapidrecognitionofpneumonia from chest X-ray images.IEEE Journal of Biomedical and Health Informatics, 28(2):753–764, 2023.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging InterpretableCNN-multilevelattentiontransformerforrapidrecognitionofpneumonia from chest X-ray images.IEEE Journal of Biomedical and Health Informatics, 28(2):753–764, 2023

Reference 1

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Observation 03973852-fe91-4b04-b0d2-752c6751582c · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Communication-efficient learning of deep networks from decentralized data

Reference 2

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Observation dfec37dd-53e5-4fa2-a8ca-3976fe5fefad · outbound

This paper cites FLamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings.Advances in Neural Information Processing Systems, 35:5315–5334, 2022.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging FLamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings.Advances in Neural Information Processing Systems, 35:5315–5334, 2022

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.405895Z

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Observation 3f3b43d2-c3ee-485f-8dfa-64dd19ac2975 · outbound

This paper cites FedEraser: Enabling efficient client-level data removal from federated learning models.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging FedEraser: Enabling efficient client-level data removal from federated learning models

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.392239Z

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Observation 64e6ca09-3d5b-449e-8fd3-5ed93b3ea403 · outbound

This paper cites Federated Unlearning: How to Efficiently Erase a Client in FL?.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 5

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Observation 7f9e23f9-b823-401d-9ef2-0b05028cdf65 · outbound

This paper cites Federated unlearning: A survey on methods, design guidelines, and evaluation metrics.IEEE transactions on neural networks and learning systems, pages 1–21, 2024.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Federated unlearning: A survey on methods, design guidelines, and evaluation metrics.IEEE transactions on neural networks and learning systems, pages 1–21, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.375246Z

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Observation 76e83e1b-d93a-451f-aa08-c67494f1ed23 · outbound

This paper cites Deep Unlearn: Benchmarking Machine Unlearning for Image Classification.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Deep Unlearn: Benchmarking Machine Unlearning for Image Classification

Reference 7

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Observation b20f36c2-89bb-4f18-bb7c-e5dbfbd7684b · outbound

This paper cites Enable the right to be forgotten with federated client unlearning in medical imaging.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Enable the right to be forgotten with federated client unlearning in medical imaging

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.357817Z

Source-reported events for the cited work

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Observation 8fa14aa1-3b8e-4272-8e3f-732a7e4b05f9 · outbound

This paper cites Maverick:Collaboration-freefederatedunlearning formedicalprivacy.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Maverick:Collaboration-freefederatedunlearning formedicalprivacy

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.333716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5e30b31e-28c2-4280-b95c-76a6982c5015 · outbound

This paper cites Restyled, tuning, and alignment: Taming VLMs for federated non-IID medical image analysis.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Restyled, tuning, and alignment: Taming VLMs for federated non-IID medical image analysis

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.319221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5ecb56fc-6c5c-4086-9c73-c03e77aefe82 · outbound

This paper cites Taming vision-language models for federated founda- tion models on heterogeneous medical imaging modalities.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Taming vision-language models for federated founda- tion models on heterogeneous medical imaging modalities

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.304543Z

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Observation 35886b88-48fb-4c69-96f4-e534cce2256e · outbound

This paper cites Visual and textual spaces both matter: Taming CLIP fornon-IIDfederatedmedicalimageclassification.Knowledge-BasedSystems,338:115524, 2026.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Visual and textual spaces both matter: Taming CLIP fornon-IIDfederatedmedicalimageclassification.Knowledge-BasedSystems,338:115524, 2026

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.289646Z

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Observation 5eb45309-9565-415e-aa84-86c62348c9d5 · outbound

This paper cites Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T15:16:43.364013Z

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Observation fbe0d8a9-09db-4f60-9b65-c2798b8ca98c · outbound

This paper cites Federated Prompt Learning for Weather Foundation Models on Devices.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Federated Prompt Learning for Weather Foundation Models on Devices

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T15:16:43.373454Z

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Observation 4e4ece5f-54e4-4bec-8b95-f76a5049f0fd · outbound

This paper cites Federatedfoundation models on heterogeneous time series.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Federatedfoundation models on heterogeneous time series

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.272202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e0ebe7b6-e79a-4958-8a90-5340c99ae94f · outbound

This paper cites FeDaL: Federated dataset learning for general time series foundation models.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging FeDaL: Federated dataset learning for general time series foundation models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.250671Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6c7a7f65-96c4-427f-a77a-fac9292256ea · outbound

This paper cites Federated unlearning via class- discriminative pruning.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Federated unlearning via class- discriminative pruning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.227754Z

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Observation eb779117-3baf-4767-849b-cb552e20e60b · outbound

This paper cites FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T15:16:43.395600Z

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Observation c44e681f-48f8-46a1-95f0-8025fd2e0b97 · outbound

This paper cites Federatedunlearningusingdiffusivenoiseinjection.InformationFusion,page 103796, 2025.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Federatedunlearningusingdiffusivenoiseinjection.InformationFusion,page 103796, 2025

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.205746Z

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Observation 26c6db8b-7d54-4221-be03-787f4eb85704 · outbound

This paper cites Unlearning through knowledge overwriting: Reversible federated unlearning via selective sparse adapter.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Unlearning through knowledge overwriting: Reversible federated unlearning via selective sparse adapter

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.192040Z

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Observation 6637ea91-24cb-47db-b04c-446c439cff9b · outbound

This paper cites FedCARE:Federatedunlearningwithconflict-awareprojectionandrelearning-resistant recovery.arXiv preprint arXiv:2601.22589, 2026.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging FedCARE:Federatedunlearningwithconflict-awareprojectionandrelearning-resistant recovery.arXiv preprint arXiv:2601.22589, 2026

Reference 21

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Observation b208e2a5-9baa-4df2-80c9-f14961f67e38 · outbound

This paper cites Membership inference attacks against machine learning models.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Membership inference attacks against machine learning models

Reference 22

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Observation f8def0ad-7bcd-411e-8895-feaa11c68697 · outbound

This paper cites Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T15:16:43.423029Z

Source-reported events for the cited work

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Observation f3f9888d-0423-47ca-ac8c-336a2e66da15 · outbound

This paper cites Membership inference attacks from first principles.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Membership inference attacks from first principles

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T15:16:43.429153Z

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Observation 594dc197-d0e0-4ef9-98bf-f14fa5d9755e · outbound

This paper cites Inexact unlearning needs more careful evaluations to avoid a false sense of privacy.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Inexact unlearning needs more careful evaluations to avoid a false sense of privacy

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.151789Z

Source-reported events for the cited work

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Observation 9ebf954f-e0ad-420a-be5e-b0e03b80abd4 · outbound

This paper cites MedMNIST v2-a large-scale lightweight benchmark for 2D and 3D biomedical image classification.Scientific data, 10(1):41, 2023.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging MedMNIST v2-a large-scale lightweight benchmark for 2D and 3D biomedical image classification.Scientific data, 10(1):41, 2023

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.140087Z

Source-reported events for the cited work

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Observation 4e1122a4-4e0f-433b-8a62-da61bf3e89d5 · outbound

This paper cites Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.127778Z

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Observation 2349832c-c919-4b47-9d2d-b44c87c663b5 · outbound

This paper cites From detection of individual metastases to classification of lymph node statusatthepatientlevel:thecamelyon17challenge.IEEEtransactionsonmedicalimaging, 38(2):550–560, 2018.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging From detection of individual metastases to classification of lymph node statusatthepatientlevel:thecamelyon17challenge.IEEEtransactionsonmedicalimaging, 38(2):550–560, 2018

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.115926Z

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Observation a1f31602-9f55-4931-9a96-74d5e000c54e · outbound

This paper cites WILDS: A benchmark of in-the-wild distribution shifts.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging WILDS: A benchmark of in-the-wild distribution shifts

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.103235Z

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Observation 0028b83c-7c47-4463-9359-28f55835816d · outbound

This paper cites Kvasir-SEG: A segmented polyp dataset.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Kvasir-SEG: A segmented polyp dataset

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.090456Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c1c172d6-6f54-4aca-b825-187bf5fc11a9 · outbound

This paper cites WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.077717Z

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Observation 966c301c-b664-4ef1-81fa-a8fe0ab43396 · outbound

This paper cites Skin lesion analysis toward melanoma detection: A challenge at the 2017 internationalsymposiumonbiomedicalimaging(isbi),hostedbytheinternationalskin imaging collaboration (isic).

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Skin lesion analysis toward melanoma detection: A challenge at the 2017 internationalsymposiumonbiomedicalimaging(isbi),hostedbytheinternationalskin imaging collaboration (isic)

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.065460Z

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Observation 7b423b4c-a879-46c0-8436-154f226f8c9c · outbound

This paper cites McCollough, Adam C.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging McCollough, Adam C

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.053904Z

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Observation 50011384-2b6a-4fa8-a10e-a8149c41a600 · outbound

This paper cites IXI dataset — informa- tion extraction from images.https://brain-development.org/ixi-dataset/, 2007.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging IXI dataset — informa- tion extraction from images.https://brain-development.org/ixi-dataset/, 2007

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.031184Z

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Observation ab9ab6bf-6d8f-495a-8dc1-8608d85d5594 · outbound

This paper cites A dataset of clinically generated visual questions and answers about radiology images.Scientific data, 5(1):180251, 2018.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging A dataset of clinically generated visual questions and answers about radiology images.Scientific data, 5(1):180251, 2018

Reference 35

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Observation 6dfd4cc5-2dbd-40df-b25f-2d71104e2083 · outbound

This paper cites SLAKE: A semantically-labeled knowledge-enhanced dataset for medical visual question answer- ing.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging SLAKE: A semantically-labeled knowledge-enhanced dataset for medical visual question answer- ing

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:44.012326Z

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Observation a841569c-169f-41d4-a669-cea724211f92 · outbound

This paper cites PathVQA: 30000+ Questions for Medical Visual Question Answering.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging PathVQA: 30000+ Questions for Medical Visual Question Answering

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T15:16:43.530053Z

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Observation 455ae7a5-fb34-42e5-9e89-fa542c448e3f · outbound

This paper cites Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 38

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Observation 518a1629-176d-4077-88b5-25d2a16f6177 · outbound

This paper cites Federated unlearning made practical: Seamless integration via negated pseudo-gradients.IEEE Transactions on Big Data, 2026.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Federated unlearning made practical: Seamless integration via negated pseudo-gradients.IEEE Transactions on Big Data, 2026

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:43.999898Z

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Observation 7465a8a8-4d08-4e63-be90-d4718e7cccf9 · outbound

This paper cites an unresolved cited work.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:16:43.986457Z

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Observation f9f23cb9-ffb2-4454-a5ed-bc2d6739925b · outbound

This paper cites BFU:Bayesianfederated unlearning with parameter self-sharing.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging BFU:Bayesianfederated unlearning with parameter self-sharing

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:43.973456Z

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Observation 071635de-5c3c-4282-ab23-1ddd92c758e3 · outbound

This paper cites NoT: Federated unlearning via weight negation.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging NoT: Federated unlearning via weight negation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:43.960724Z

Source-reported events for the cited work

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Observation e6033a80-23ae-4dc5-90d2-f6eb51e04d31 · outbound

This paper cites Deep residual learning for image recognition: A survey.Applied sciences, 12(18):8972, 2022.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Deep residual learning for image recognition: A survey.Applied sciences, 12(18):8972, 2022

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T15:16:43.572401Z

Source-reported events for the cited work

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Observation aeed6595-1610-4689-b1c0-8e893344f241 · outbound

This paper cites U-net:Convolutionalnetworksfor biomedical image segmentation.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging U-net:Convolutionalnetworksfor biomedical image segmentation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:43.937201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 750dcdf2-a7fc-4b68-8358-3591d5fc2624 · outbound

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

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T15:16:43.586482Z

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Observation f7cba35c-d801-49a6-bec0-ecbd6d403550 · outbound

This paper cites VoxelMorph: A Learning Framework for Deformable Medical Image Registration.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging VoxelMorph: A Learning Framework for Deformable Medical Image Registration

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Resolution: unresolved; no resolver link, observed 2026-08-15T15:16:43.593367Z

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Observation e1db0bd6-996e-4d74-adaa-86518bd70a35 · outbound

This paper cites Personalizedadapter forlargemeteorologymodelondevices:Towardsweatherfoundationmodels.Advances in Neural Information Processing Systems, 37:84897–84943, 2024.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Personalizedadapter forlargemeteorologymodelondevices:Towardsweatherfoundationmodels.Advances in Neural Information Processing Systems, 37:84897–84943, 2024

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:16:43.924560Z

Source-reported events for the cited work

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Observation a4c3826b-9481-4fa9-a762-94eb81764bdf · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Imagenet: A large-scale hierarchical image database

Reference 48

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Observation 3779b4a4-6be5-4a52-873e-cbfa78fe1ff8 · outbound

This paper cites Statistical comparisons of classifiers over multiple data sets.Journal of Machine learning research, 7(Jan):1–30, 2006.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Statistical comparisons of classifiers over multiple data sets.Journal of Machine learning research, 7(Jan):1–30, 2006

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T15:16:43.619367Z

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Observation 6e9d76b2-b2ca-4d31-a876-b7a56f7f27e8 · outbound

This paper cites Visualizing data using t-sne.Journal of Machine Learning Research, 9:2579–2605, 2008.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Visualizing data using t-sne.Journal of Machine Learning Research, 9:2579–2605, 2008

Reference 50

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T15:16:43.894958Z

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Observation bacb61bb-3c60-45ee-99c9-fd20e79c70de · outbound

This paper cites an unresolved cited work.

Lethe: How Hard Is It to Forget? A Benchmark for Federated Unlearning in Medical Imaging Unresolved cited work

Reference 2017

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:16:44.042663Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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