FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis | Paper Citation Record

Observation 9a93d0a7-be42-46c6-88e2-1007ff183afd · outbound

This paper cites Deep learning-based image quality as- sessment for optical coherence tomography macular scans: a multicentre study,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Deep learning-based image quality as- sessment for optical coherence tomography macular scans: a multicentre study,

Reference 1

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This paper cites Using deep learning for assessing image- quality of 3d macular scans from spectral-domain optical coherence tomography,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Using deep learning for assessing image- quality of 3d macular scans from spectral-domain optical coherence tomography,

Reference 2

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This paper cites The future of digital health with federated learning,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis The future of digital health with federated learning,

Reference 3

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This paper cites Regulation (eu) 2016/679 of the european parliament and of the council,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Regulation (eu) 2016/679 of the european parliament and of the council,

Reference 4

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Observation 6c13b1d2-61f5-4eb5-97d7-12e6c84bc482 · outbound

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

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Communication-efficient learning of deep networks from decentralized data,

Reference 5

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Observation 863d12ee-4aa3-49eb-b1d0-3f155d2f8363 · outbound

This paper cites Out-of-distribution generalization of federated learning via implicit invariant relationships,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Out-of-distribution generalization of federated learning via implicit invariant relationships,

Reference 6

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This paper cites Anomaly detection under distribution shift,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Anomaly detection under distribution shift,

Reference 7

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Observation 3503579c-068c-4756-8f20-cf01058cc643 · outbound

This paper cites Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Feddg: Federated domain generalization on medical image segmentation via episodic learning in continuous frequency space,

Reference 8

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Observation 54693709-0d56-4476-ba5a-1d98bbd23843 · outbound

This paper cites A deep learning system for predicting time to progression of diabetic retinopathy,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis A deep learning system for predicting time to progression of diabetic retinopathy,

Reference 9

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Observation c6ee1c2e-e3d9-42ed-830e-da5248a816f2 · outbound

This paper cites Unsupervised domain adaptation for anatomical landmark detection,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Unsupervised domain adaptation for anatomical landmark detection,

Reference 10

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Observation 47cbf7c9-bb8c-4d27-8706-e75e433d5529 · outbound

This paper cites Federated domain general- ization for image recognition via cross-client style transfer,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Federated domain general- ization for image recognition via cross-client style transfer,

Reference 11

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Observation 1152ab21-e3f5-415e-8ae0-fe65cb124cf5 · outbound

This paper cites Efficient federated domain translation,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Efficient federated domain translation,

Reference 12

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Observation 044a82fa-7be8-4be3-b5aa-6be7d720af01 · outbound

This paper cites Stablefdg: Style and attention based learning for federated domain generalization,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Stablefdg: Style and attention based learning for federated domain generalization,

Reference 13

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Observation 144f3022-c9c8-4681-a29d-079e3697d080 · outbound

This paper cites Federated adversar- ial domain hallucination for privacy-preserving domain generalization,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Federated adversar- ial domain hallucination for privacy-preserving domain generalization,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:44.067471Z

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This paper cites Fedsr: A simple and effective domain generalization method for federated learning,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Fedsr: A simple and effective domain generalization method for federated learning,

Reference 15

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Observation 2110de1b-b5dc-4644-b069-d3772632dd76 · outbound

This paper cites Closing the generalization gap of cross-silo federated medical image segmentation,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Closing the generalization gap of cross-silo federated medical image segmentation,

Reference 16

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Observation aacc3b97-0943-401a-84ab-cdf5bc55a772 · outbound

This paper cites Federated learning for iot devices with domain generalization,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Federated learning for iot devices with domain generalization,

Reference 17

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Observation 8321a7a4-666e-4579-bca2-4d0fc01ff061 · outbound

This paper cites Federated domain generalization with generalization adjustment,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Federated domain generalization with generalization adjustment,

Reference 18

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Observation 8cf327f4-a261-4690-bc98-4c60862bbe26 · outbound

This paper cites Collaborative semantic aggregation and calibration for federated domain generaliza- tion,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Collaborative semantic aggregation and calibration for federated domain generaliza- tion,

Reference 19

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Observation 1736d22c-20f9-4466-9e8c-ecbf23b70c3f · outbound

This paper cites Iop-fl: inside-outside personalization for federated medical image segmentation,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Iop-fl: inside-outside personalization for federated medical image segmentation,

Reference 20

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Observation 65e25f4e-06f0-4e9e-8cf1-8d2a3539af6d · outbound

This paper cites Domain-aware dual attention for generalized medical image segmentation on unseen domains,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Domain-aware dual attention for generalized medical image segmentation on unseen domains,

Reference 21

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Observation 2e382162-4851-45d7-ab85-e1c4e0b092f1 · outbound

This paper cites Efficiently assemble normalization layers and regularization for federated domain generalization,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Efficiently assemble normalization layers and regularization for federated domain generalization,

Reference 22

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Observation 6bcbf8e6-d358-48ea-ac81-6f07f8ee5f02 · outbound

This paper cites Rfdg: Reinforcement federated domain generalization,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Rfdg: Reinforcement federated domain generalization,

Reference 23

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This paper cites Fraug: Tackling federated learning with non-iid features via representation augmentation,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Fraug: Tackling federated learning with non-iid features via representation augmentation,

Reference 24

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This paper cites G2g: Generalized learning by cross- domain knowledge transfer for federated domain generalization,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis G2g: Generalized learning by cross- domain knowledge transfer for federated domain generalization,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:43.694985Z

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This paper cites Beyond the federation: Topology-aware federated learning for generalization to unseen clients,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Beyond the federation: Topology-aware federated learning for generalization to unseen clients,

Reference 26

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This paper cites Diprompt: Disentangled prompt tuning for multiple latent domain generalization in federated learning,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Diprompt: Disentangled prompt tuning for multiple latent domain generalization in federated learning,

Reference 27

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Observation 5a50026a-e4fa-4055-9b7a-9bea277dd0b5 · outbound

This paper cites Dafkd: Domain- aware federated knowledge distillation,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Dafkd: Domain- aware federated knowledge distillation,

Reference 28

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Observation 59fdd27a-6034-4b44-97c0-0d4ea272b38f · outbound

This paper cites Learning to generate novel domains for domain generalization,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Learning to generate novel domains for domain generalization,

Reference 29

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Observation 53681ea6-0d6f-41bb-9e26-ce7f5cae327b · outbound

This paper cites Deep domain-adversarial image generation for domain generali- sation,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Deep domain-adversarial image generation for domain generali- sation,

Reference 30

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This paper cites Adversar- ial teacher-student representation learning for domain generalization,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Adversar- ial teacher-student representation learning for domain generalization,

Reference 31

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Observation 9b8b668a-e810-46ff-98e9-b63f52f9be75 · outbound

This paper cites Learning to aug- ment via implicit differentiation for domain generalization,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Learning to aug- ment via implicit differentiation for domain generalization,

Reference 32

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Observation 9fdd6e35-b16b-4e9c-81cf-d3d67a5dced8 · outbound

This paper cites Wasserstein generative adver- sarial networks,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Wasserstein generative adver- sarial networks,

Reference 33

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This paper cites A Style-Based Generator Architecture for Generative Adversarial Networks.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis A Style-Based Generator Architecture for Generative Adversarial Networks

Reference 34

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This paper cites Denoising diffusion probabilistic models,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Denoising diffusion probabilistic models,

Reference 35

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Observation 96166baa-4faa-4215-84b4-8dd2b35db5cd · outbound

This paper cites Improved denoising diffusion proba- bilistic models,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Improved denoising diffusion proba- bilistic models,

Reference 36

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This paper cites High-resolution image synthesis with latent diffusion models,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis High-resolution image synthesis with latent diffusion models,

Reference 37

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Observation c953cec9-95f5-4cef-acdf-a4d20eeb1681 · outbound

This paper cites Unpaired optical coherence tomog- raphy angiography image super-resolution via frequency-aware inverse- consistency gan,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Unpaired optical coherence tomog- raphy angiography image super-resolution via frequency-aware inverse- consistency gan,

Reference 38

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Observation 1445e63d-2f79-4330-976d-cbcdb690716f · outbound

This paper cites Towards generalizable diabetic retinopathy grading in unseen domains,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Towards generalizable diabetic retinopathy grading in unseen domains,

Reference 39

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This paper cites Learning robust representation for joint grading of ophthalmic diseases via adaptive curriculum and feature disentanglement,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Learning robust representation for joint grading of ophthalmic diseases via adaptive curriculum and feature disentanglement,

Reference 40

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This paper cites Domain general- ization: A survey,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Domain general- ization: A survey,

Reference 41

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Observation 35cb2199-41d6-4696-a999-d507c6b6b379 · outbound

This paper cites Image quality-aware diagnosis via meta- knowledge co-embedding,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Image quality-aware diagnosis via meta- knowledge co-embedding,

Reference 42

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Observation 09c8dcd7-09a7-4549-8620-65547f268aae · outbound

This paper cites The effect of intrinsic dataset properties on generalization: Unraveling learning differences between natural and medical images,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis The effect of intrinsic dataset properties on generalization: Unraveling learning differences between natural and medical images,

Reference 43

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Observation dfe4bee3-d7e5-4d81-bc47-958e4ec590e3 · outbound

This paper cites DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images

Reference 44

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Observation 5345d4e0-d14b-4a6b-a4c5-8a3953382735 · outbound

This paper cites Rethinking Self-training for Semi-supervised Landmark Detection: A Selection-free Approach.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Rethinking Self-training for Semi-supervised Landmark Detection: A Selection-free Approach

Reference 45

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Observation 38b9118b-21f4-4ea6-be1a-134dbff2e13f · outbound

This paper cites What do we mean by generalization in federated learning?.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis What do we mean by generalization in federated learning?

Reference 46

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Observation 798f4a4d-d5fc-4ab0-b5ac-7e8758d6e7d3 · outbound

This paper cites Unified deep supervised domain adaptation and generalization,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Unified deep supervised domain adaptation and generalization,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.965970Z

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Observation b895c3d1-fcc5-4bb5-b52f-3dd9110e3ff3 · outbound

This paper cites Multi-adversarial discriminative deep domain generalization for face presentation attack detection,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Multi-adversarial discriminative deep domain generalization for face presentation attack detection,

Reference 48

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Observation ea38e3f9-60a6-416b-b8c4-453be0102999 · outbound

This paper cites Do- main generalization via model-agnostic learning of semantic features,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Do- main generalization via model-agnostic learning of semantic features,

Reference 49

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Observation ac2f43f4-3aee-4ca6-b514-907076ba0280 · outbound

This paper cites Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark

Reference 50

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This paper cites No one left behind: Real-world federated class-incremental learning,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis No one left behind: Real-world federated class-incremental learning,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.681373Z

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Observation d83825ad-7f2d-49c6-81c1-baec7341c824 · outbound

This paper cites Fedseg: Class-heterogeneous federated learning for semantic segmentation,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Fedseg: Class-heterogeneous federated learning for semantic segmentation,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.619144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b02c9a74-fac6-4b9f-b3b3-27845e4205f1 · outbound

This paper cites Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T16:58:40.764466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 921c593b-e2f9-46c1-a0d7-355d09021697 · outbound

This paper cites Virtual homogeneity learning: Defending against data heterogeneity in federated learning,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Virtual homogeneity learning: Defending against data heterogeneity in federated learning,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.605163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 63567c5c-dcca-4637-b562-92e7f4bfd6e6 · outbound

This paper cites Federated Adversarial Domain Adaptation.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Federated Adversarial Domain Adaptation

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-10T16:58:40.774331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71b06c49-a696-4876-b16d-8c304ddd07a4 · outbound

This paper cites Collaborative heterogeneous causal inference beyond meta-analysis,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Collaborative heterogeneous causal inference beyond meta-analysis,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.591392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6581b927-d0ad-4a98-b1f9-6183bd4a92bb · outbound

This paper cites Prompt Public Large Language Models to Synthesize Data for Private On-device Applications.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Prompt Public Large Language Models to Synthesize Data for Private On-device Applications

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-10T16:58:40.783408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40480d00-2c80-474f-887e-aae63efcc52f · outbound

This paper cites Promptmrg: Diagnosis-driven prompts for medical report generation,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Promptmrg: Diagnosis-driven prompts for medical report generation,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.575651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8c24fb7f-81cb-44d8-bffd-0a31ee58d7de · outbound

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

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Mean teachers are better role mod- els: Weight-averaged consistency targets improve semi-supervised deep learning results,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.561465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a40cc62b-fe20-41f1-8ec8-382cac11ee8f · outbound

This paper cites Sharpness-aware minimization for efficiently improving generalization,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Sharpness-aware minimization for efficiently improving generalization,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.522418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f7d5f0cd-0458-4689-a09a-d565288e2460 · outbound

This paper cites Improving the model consistency of decentralized federated learning,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Improving the model consistency of decentralized federated learning,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.363874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 033b4d8c-fee1-40af-9ac6-078df6019d30 · outbound

This paper cites Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-10T16:58:40.805446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 886d1bdf-80cd-4bf7-9102-d0f11b8a2493 · outbound

This paper cites Window-based model averaging improves generalization in heterogeneous federated learning,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Window-based model averaging improves generalization in heterogeneous federated learning,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.332058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 495ce2c7-5a33-4caa-84ae-afda35dd7932 · outbound

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

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Swad: Domain generalization by seeking flat minima,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.318792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation acf10424-7bae-4d0a-8b88-7ec7872ae4aa · outbound

This paper cites Fedbn: Federated learning on non-iid features via local batch normalization,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Fedbn: Federated learning on non-iid features via local batch normalization,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.303852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0409fab9-cf49-4e3a-af19-80d8139d4824 · outbound

This paper cites Federated optimization in heterogeneous networks,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Federated optimization in heterogeneous networks,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.288789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 72b47611-aa58-4d37-8e00-dbe9e95b414e · outbound

This paper cites Learn from others and be yourself in heterogeneous federated learning,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Learn from others and be yourself in heterogeneous federated learning,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.274112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6ae8a7ee-7921-459f-b5ff-a5a63e1a13a6 · outbound

This paper cites Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.260212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 544b4430-d17e-48f9-bceb-aceba20b67bc · outbound

This paper cites Self-challenging improves cross-domain generalization,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Self-challenging improves cross-domain generalization,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.246239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 284d4ba9-93ca-495e-99d7-ecdfb12e8e01 · outbound

This paper cites Domain generalization with mixstyle,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Domain generalization with mixstyle,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.232266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 47de2f01-d435-4ac5-9d9d-a02aa14c5188 · outbound

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

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Wilds: A benchmark of in-the-wild distribution shifts,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:42.141862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ed58e209-3e0a-4af7-ac02-2cee846eb298 · outbound

This paper cites Mitosis domain generalization challenge 2022,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Mitosis domain generalization challenge 2022,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:41.939969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b667fc4e-c1bb-420c-865d-5479330b9014 · outbound

This paper cites Flamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Flamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:41.926096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dd4ce40e-2de0-4de7-8074-8f0b943940b8 · outbound

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

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:41.911507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0625fe0b-8d61-4d1e-8f0b-1fe6f0bec948 · outbound

This paper cites an unresolved cited work.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-10T16:58:41.896469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 96aeda2b-ed5a-4b68-b138-79e5c3a3c4bd · outbound

This paper cites Bcn20000: Dermoscopic lesions in the wild,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Bcn20000: Dermoscopic lesions in the wild,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:41.881815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8b7540fa-ba26-49b8-a309-b875abfe2399 · outbound

This paper cites Loss surfaces, mode connectivity, and fast ensembling of dnns,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Loss surfaces, mode connectivity, and fast ensembling of dnns,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:41.835672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 781fb7fe-fc92-435c-9dc3-de3006905e4d · outbound

This paper cites Improving generalization in federated learning by seeking flat minima,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Improving generalization in federated learning by seeking flat minima,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:41.749553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3af94070-0873-4691-961b-9d789dabb1fb · outbound

This paper cites Pain-fl: Personalized privacy-preserving incentive for federated learning,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Pain-fl: Personalized privacy-preserving incentive for federated learning,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:41.734605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 869624fd-d1ae-409b-8de3-e071a4b18922 · outbound

This paper cites Micronet: Improving image recognition with extremely low flops,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Micronet: Improving image recognition with extremely low flops,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:41.719648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aaacd507-5438-489c-b4d9-7ae9be41dcc2 · outbound

This paper cites Fedgcn: Convergence- communication tradeoffs in federated training of graph convolutional networks,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Fedgcn: Convergence- communication tradeoffs in federated training of graph convolutional networks,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:41.704265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0933ea69-5a56-4e2f-8cf0-53c2b870d235 · outbound

This paper cites Deeper, broader and artier domain generalization,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Deeper, broader and artier domain generalization,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:58:41.688611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cc8ec677-4b67-45f2-b547-0ba107fde20b · outbound

This paper cites Image style transfer using convolutional neural networks,.

FedDAG: Federated Domain Adversarial Generation Towards Generalizable Medical Image Analysis Image style transfer using convolutional neural networks,

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-10T16:58:41.157677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers