Paper Citation Record · LEDGER

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook

As of 10 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 2 inbound Pith citation observations for arXiv:2512.09315.

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

pith.paper-citation-record.v1
2512.09315 v1

Coverage vector

measured 56 of 56 reference resolution

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One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

56 of 56 outbound references displayed

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External citation measurements

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Outbound references

Observation ad5da691-1746-496b-bafd-61ecd9fe5991 · outbound

This paper cites Two wrongs don’t make a right: Combating confirmation bias in learning withlabelnoise,in:ProceedingsoftheAAAIConferenceonArtificial Intelligence, pp.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Two wrongs don’t make a right: Combating confirmation bias in learning withlabelnoise,in:ProceedingsoftheAAAIConferenceonArtificial Intelligence, pp

Reference 1

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Observation 191f62ff-ac5c-48d2-a7b9-ad8ae47f7809 · outbound

This paper cites Understanding and utilizing deep neural networks trained with noisy labels, in: International conference on machine learning, PMLR.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Understanding and utilizing deep neural networks trained with noisy labels, in: International conference on machine learning, PMLR

Reference 2

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Observation a3643ed5-ea13-4e8e-94cb-8c6505ef95de · outbound

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Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Unresolved cited work

Reference 3

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Observation 9aea1e67-2d34-4e7b-99c7-59438cfe89f9 · outbound

This paper cites Training a neural network based on unreliable human annotation of medical images, in: 2018 IEEE 15th International symposium on biomedical imaging (ISBI 2018), IEEE.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Training a neural network based on unreliable human annotation of medical images, in: 2018 IEEE 15th International symposium on biomedical imaging (ISBI 2018), IEEE

Reference 4

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Observation ff1907f6-5e25-4405-8eb8-fe7764c7434f · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 5

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Observation 61c410d0-d251-4b11-b14b-efdb8e55b605 · outbound

This paper cites A cnn-based unified frame- work utilizing projection loss in unison with label noise handling for multiple myeloma cancer diagnosis.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook A cnn-based unified frame- work utilizing projection loss in unison with label noise handling for multiple myeloma cancer diagnosis

Reference 6

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Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Unresolved cited work

Reference 7

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Observation d8147769-180a-481a-bac0-b57f01d538de · outbound

This paper cites Openmibood: Open medical imaging benchmarks for out-of-distribution detection, in: Proceedings of the Computer Vision and Pattern Recognition Conference, pp.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Openmibood: Open medical imaging benchmarks for out-of-distribution detection, in: Proceedings of the Computer Vision and Pattern Recognition Conference, pp

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.489559Z

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Observation da616687-b960-459d-95b8-fb5d31edf1c8 · outbound

This paper cites Co-teaching:Robusttrainingofdeepneuralnetworkswith extremely noisy labels.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Co-teaching:Robusttrainingofdeepneuralnetworkswith extremely noisy labels

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.484095Z

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Observation 61d4a688-f7da-4b8c-aa1e-7a8188bc1eca · outbound

This paper cites Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

Reference 10

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Observation 286fce68-e235-4f0c-b06d-4921109448c9 · outbound

This paper cites Using pre-training can improve model robustness and uncertainty, in: International confer- ence on machine learning, PMLR.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Using pre-training can improve model robustness and uncertainty, in: International confer- ence on machine learning, PMLR

Reference 11

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Observation 8c0362d0-2708-46b8-8923-076489005a51 · outbound

This paper cites Qmix: Quality-aware learning with mixed noise for robust retinal disease diagnosis.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Qmix: Quality-aware learning with mixed noise for robust retinal disease diagnosis

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.523015Z

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Observation 053e3560-43d0-4c31-bbe3-874065a5a5ee · outbound

This paper cites Cross-field transformer for diabetic retinopathy gradingontwo-fieldfundusimages,in:2022IEEEInternationalCon- ferenceonBioinformaticsandBiomedicine(BIBM),IEEEComputer Society.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Cross-field transformer for diabetic retinopathy gradingontwo-fieldfundusimages,in:2022IEEEInternationalCon- ferenceonBioinformaticsandBiomedicine(BIBM),IEEEComputer Society

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.526039Z

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Observation c5064279-9e25-4f2e-b9f8-7b84ab71c7c0 · outbound

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

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs

Reference 14

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Observation 43ffa2b9-6681-4681-9e3b-6958bcc9d9d1 · outbound

This paper cites Improving medical images classification with label noise using dual-uncertainty estimation.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Improving medical images classification with label noise using dual-uncertainty estimation

Reference 15

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Observation 66243663-9c3c-442a-ac6e-a9985cbbd925 · outbound

This paper cites MONICA: Benchmarking on Long-tailed Medical Image Classification.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook MONICA: Benchmarking on Long-tailed Medical Image Classification

Reference 16

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Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Unresolved cited work

Reference 17

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Observation 158e3e42-ca44-47de-8555-78e4fa8dad86 · outbound

This paper cites IEEETransactionsonMedicalImaging43,335– 350.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook IEEETransactionsonMedicalImaging43,335– 350

Reference 18

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Observation e75609ff-2df2-4a36-8e22-47df3e4860ba · outbound

This paper cites Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis

Reference 19

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Observation c7649f20-b197-4539-ba15-561d6af5474a · outbound

This paper cites Improving medicalimageclassificationinnoisylabelsusingonlyself-supervised pretraining, in: MICCAI Workshop on Data Engineering in Medical Imaging, Springer.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Improving medicalimageclassificationinnoisylabelsusingonlyself-supervised pretraining, in: MICCAI Workshop on Data Engineering in Medical Imaging, Springer

Reference 20

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Observation a708d575-3f52-4471-9baf-335b4d0c89ad · outbound

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Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Unresolved cited work

Reference 21

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Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Unresolved cited work

Reference 22

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This paper cites DivideMix: Learning with Noisy Labels as Semi-supervised Learning.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 23

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This paper cites Provably end- to-end label-noise learning without anchor points, in: International conference on machine learning, PMLR.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Provably end- to-end label-noise learning without anchor points, in: International conference on machine learning, PMLR

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.558303Z

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Observation 647ccc8c-4a5e-471e-83f0-ae75c5424d29 · outbound

This paper cites Disc: Learning from noisy labels via dynamic instance-specific selection and correction, in:ProceedingsoftheIEEE/CVFconferenceoncomputervisionand pattern recognition, pp.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Disc: Learning from noisy labels via dynamic instance-specific selection and correction, in:ProceedingsoftheIEEE/CVFconferenceoncomputervisionand pattern recognition, pp

Reference 25

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Observation 6a522b41-eacc-41ca-ac79-d27c91c426f2 · outbound

This paper cites Instance-dependent label distribution estimation for learning with label noise.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Instance-dependent label distribution estimation for learning with label noise

Reference 26

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Observation c444fd85-eda9-4f54-8084-314b31e34e79 · outbound

This paper cites Unleashing the potential of open-set noisy samples against label noise for medical image classification.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Unleashing the potential of open-set noisy samples against label noise for medical image classification

Reference 27

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Observation 7b66bf80-3a16-44a2-b1cd-5b412407852b · outbound

This paper cites Learning the latent causal structure formodelinglabelnoise.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Learning the latent causal structure formodelinglabelnoise

Reference 28

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Observation e5eda713-bf37-4720-83af-1c18cbe6fe73 · outbound

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Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Unresolved cited work

Reference 29

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This paper cites Medical image analysis 42, 60–88.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Medical image analysis 42, 60–88

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.550698Z

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This paper cites 2537–2546.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook 2537–2546

Reference 31

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This paper cites Does label smoothing mitigate label noise?, in: International Conference on Machine Learning, PMLR.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Does label smoothing mitigate label noise?, in: International Conference on Machine Learning, PMLR

Reference 32

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Observation d52f9f2f-d031-48f8-80e0-28cfd7a95905 · outbound

This paper cites Bench- marking common uncertainty estimation methods with histopatho- logical images under domain shift and label noise.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Bench- marking common uncertainty estimation methods with histopatho- logical images under domain shift and label noise

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.542939Z

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This paper cites The multimodal brain tumor image segmentation benchmark (brats).

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook The multimodal brain tumor image segmentation benchmark (brats)

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.552897Z

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Observation 28e9f8f4-9574-42e8-982d-1656dca018b8 · outbound

This paper cites SELF: Learning to Filter Noisy Labels with Self-Ensembling.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook SELF: Learning to Filter Noisy Labels with Self-Ensembling

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:58:42.742274Z

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Observation a788e1e3-6a68-4125-a12e-afd4d04baabe · outbound

This paper cites Interpreting chest x-rays via cnns that exploit hierarchical disease dependenciesanduncertaintylabels.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Interpreting chest x-rays via cnns that exploit hierarchical disease dependenciesanduncertaintylabels

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.540167Z

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Observation 36b56178-296d-4f67-9a49-59e1121c8734 · outbound

This paper cites Asurveyof label-noise deep learning for medical image analysis.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Asurveyof label-noise deep learning for medical image analysis

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.601003Z

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Observation 2e1491de-3822-4bd4-a4ba-1a039b07469e · outbound

This paper cites Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.577054Z

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Observation dad5e120-5c50-46f3-af2f-ec6b45488bcb · outbound

This paper cites Training Convolutional Networks with Noisy Labels.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Training Convolutional Networks with Noisy Labels

Reference 39

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Observation b07ba286-ca5a-430b-b15b-fc4cf921f682 · outbound

This paper cites Learning from noisy labels by regularized estimation of annotator confusion, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Learning from noisy labels by regularized estimation of annotator confusion, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.603456Z

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This paper cites an unresolved cited work.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Unresolved cited work

Reference 41

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Observation 8491905b-dcb3-49a8-896b-96909fe6fa48 · outbound

This paper cites 2097–2106.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook 2097–2106

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.584266Z

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Observation 701898b9-8938-4d41-a336-97dea56f1157 · outbound

This paper cites Symmet- riccrossentropyforrobustlearningwithnoisylabels,in:Proceedings of the IEEE/CVF international conference on computer vision, pp.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Symmet- riccrossentropyforrobustlearningwithnoisylabels,in:Proceedings of the IEEE/CVF international conference on computer vision, pp

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.531866Z

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Observation a52b6f38-d462-4cae-8ecd-d729c1df97f3 · outbound

This paper cites Combating noisy labels by agreement: A joint training method with co-regularization, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Combating noisy labels by agreement: A joint training method with co-regularization, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.608044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d84f7459-682f-4997-9796-585d94b5aeef · outbound

This paper cites Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:58:42.729209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7d73cb75-76ea-4b64-991f-d481c3a83373 · outbound

This paper cites an unresolved cited work.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-05-16T23:58:43.560698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a3f2d4c6-c8b2-4575-b11f-f9e8d7b5328e · outbound

This paper cites 1833–1843.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook 1833–1843

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.571801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 89c0b7d6-6f06-4024-a4cd-b6b0f8d32c77 · outbound

This paper cites Robust early-learning: Hindering the memorization of noisy labels, in: International conference on learning representations.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Robust early-learning: Hindering the memorization of noisy labels, in: International conference on learning representations

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.555439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a1e1bf8a-2ed3-4acb-a732-bf8f539d17b0 · outbound

This paper cites Part-dependent label noise: Towards instance-dependent label noise.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Part-dependent label noise: Towards instance-dependent label noise

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.566576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 61253cea-9676-480a-b4e8-f289b9efd134 · outbound

This paper cites an unresolved cited work.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-05-16T23:58:43.598570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 25a0d044-0387-4b2c-b83e-6b9475aa395f · outbound

This paper cites an unresolved cited work.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-05-16T23:58:43.545476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b8e9eafb-0e10-4ea6-9979-0a612dfcb3de · outbound

This paper cites an unresolved cited work.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-05-16T23:58:43.596024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 540e2bae-8b78-4ec4-81a5-184780deef1d · outbound

This paper cites an unresolved cited work.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-05-16T23:58:43.519839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3a238aae-ff77-410c-960d-3d3c6729a9c8 · outbound

This paper cites Scientific Data 10, 41.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Scientific Data 10, 41

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.582012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6924caa8-2de3-44fe-8f06-12440eb02b77 · outbound

This paper cites Howdoesdisagreementhelpgeneralizationagainstlabelcorruption?, in: International conference on machine learning, PMLR.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Howdoesdisagreementhelpgeneralizationagainstlabelcorruption?, in: International conference on machine learning, PMLR

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.574258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e834e4ed-90e6-479b-9501-43a7c2e11657 · outbound

This paper cites Robust curriculum learning: from clean label detection to noisy label self-correction, in: Interna- tional conference on learning representations.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook Robust curriculum learning: from clean label detection to noisy label self-correction, in: Interna- tional conference on learning representations

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T23:58:43.537634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 7b23be4d-b8f1-4751-9703-656a92268e50 · inbound

Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation cites this paper.

Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-13T17:25:42.584646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 480dee00-4732-42b6-a97a-a4efe3914ed6 · inbound

SLT: Robust Quantum Neural Networks for Noisy-Label Medical Image Classification via Supermartingale-based Label Transition cites this paper.

SLT: Robust Quantum Neural Networks for Noisy-Label Medical Image Classification via Supermartingale-based Label Transition Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-02T07:05:01.773963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.