GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences | Paper Citation Record

Observation 3ba5f642-f7e2-416f-98eb-5f33d349702b · outbound

This paper cites Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and ra- diomic features.Scientific data, 4(1):1–13.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and ra- diomic features.Scientific data, 4(1):1–13

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.325381Z

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Observation 36d0c99a-2881-4bdb-a555-c6fb2fd0eb0b · outbound

This paper cites Mul- timodal disentangled variational autoencoder with game theoretic interpretability for glioma grading.IEEE jour- nal of biomedical and health informatics, 26(2):673–684.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Mul- timodal disentangled variational autoencoder with game theoretic interpretability for glioma grading.IEEE jour- nal of biomedical and health informatics, 26(2):673–684

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.328727Z

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Observation 218d5034-e598-4274-ab27-8f20b3b4ef34 · outbound

This paper cites A fully automated multimodal mri- based multi-task learning for glioma segmentation and idh genotyping.IEEE Transactions on Medical Imaging, 41(6):1520–1532.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences A fully automated multimodal mri- based multi-task learning for glioma segmentation and idh genotyping.IEEE Transactions on Medical Imaging, 41(6):1520–1532

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.276947Z

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Observation 4e9eecd0-9529-47ed-b683-9207f02f1b7d · outbound

This paper cites Fully automated hybrid approach to pre- dict the idh mutation status of gliomas via deep learning and radiomics.Neuro-oncology, 23(2):304–313.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Fully automated hybrid approach to pre- dict the idh mutation status of gliomas via deep learning and radiomics.Neuro-oncology, 23(2):304–313

Reference 4

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Observation 203b565a-6c74-4d0b-89aa-13795a92b3ee · outbound

This paper cites Decou- pled kullback-leibler divergence loss.Advances in Neural Information Processing Systems, 37:74461–74486.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Decou- pled kullback-leibler divergence loss.Advances in Neural Information Processing Systems, 37:74461–74486

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.304958Z

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Observation 0b61b48e-bc55-4a50-a5d4-0e2a59f87443 · outbound

This paper cites Vision transformer-based glioma classification using multi-modal mri and wavelet fusion.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Vision transformer-based glioma classification using multi-modal mri and wavelet fusion

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.283567Z

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Observation 7d1f8dde-fbbe-4ae7-928b-e7e7ec8d4b53 · outbound

This paper cites Glioma groups based on 1p/19q, idh, and tert promoter muta- tions in tumors.New England Journal of Medicine, 372(26):2499–2508.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Glioma groups based on 1p/19q, idh, and tert promoter muta- tions in tumors.New England Journal of Medicine, 372(26):2499–2508

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.311189Z

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Observation 09848a5f-d198-4a0d-b252-12fcd71ffe5d · outbound

This paper cites Masked au- toencoders are scalable vision learners.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Masked au- toencoders are scalable vision learners

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.301740Z

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Observation ec913045-3988-45b4-ba4f-a5342793bdf0 · outbound

This paper cites Uda-gs: A cross- center multimodal unsupervised domain adaptation frame- work for glioma segmentation.Computers in Biology and Medicine, 185:109472.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Uda-gs: A cross- center multimodal unsupervised domain adaptation frame- work for glioma segmentation.Computers in Biology and Medicine, 185:109472

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.314533Z

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Observation d3d78fea-0954-45fd-a301-68446d6e3eec · outbound

This paper cites Semi-supervised learning for medical image classification using imbalanced training data.Computer methods and programs in biomedicine, 216:106628.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Semi-supervised learning for medical image classification using imbalanced training data.Computer methods and programs in biomedicine, 216:106628

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.317962Z

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Observation be61721c-28ba-45f7-b6f7-cb60157c4ddd · outbound

This paper cites Unsupervised contour tracking of live cells by mechanical and cycle consistency losses.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Unsupervised contour tracking of live cells by mechanical and cycle consistency losses

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.257368Z

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Observation 7536e88b-ce58-4e57-80c8-d4633fc4bbd8 · outbound

This paper cites Perceptual losses for real-time style transfer and super-resolution.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Perceptual losses for real-time style transfer and super-resolution

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.308058Z

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Observation 772bde15-aa26-4286-9b28-1a2636ea6d15 · outbound

This paper cites Gcnet: Graph completion net- work for incomplete multimodal learning in conversation.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Gcnet: Graph completion net- work for incomplete multimodal learning in conversation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.270479Z

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Observation 08813550-bf70-4115-aa90-e4a317a6101b · outbound

This paper cites Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-25T03:26:35.628895Z

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Observation 9b189202-8f04-49fe-acc9-addb60b4eb1f · outbound

This paper cites The 2021 who classification of tumors of the central nervous system: a summary.Neuro- oncology, 23(8):1231–1251.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences The 2021 who classification of tumors of the central nervous system: a summary.Neuro- oncology, 23(8):1231–1251

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.263510Z

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Observation 86e5fcab-2436-4404-9f4b-d923d6a3ad62 · outbound

This paper cites Multi-modal modality- masked diffusion network for brain mri synthesis with ran- dom modality missing.IEEE Transactions on Medical Imaging, 43(7):2587–2598.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Multi-modal modality- masked diffusion network for brain mri synthesis with ran- dom modality missing.IEEE Transactions on Medical Imaging, 43(7):2587–2598

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.273823Z

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Observation 9631956e-305f-451e-a134-469019ac7a10 · outbound

This paper cites A review of the economic burden of glioblastoma and the cost effectiveness of pharmacologic treatments.Pharmacoeconomics, 32:1201–1212.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences A review of the economic burden of glioblastoma and the cost effectiveness of pharmacologic treatments.Pharmacoeconomics, 32:1201–1212

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.284269Z

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Observation 049a20da-ec2e-4760-808b-6f3fa693e16e · outbound

This paper cites Idh1 mutations as molecular signature and predictive factor of secondary glioblastomas.Clinical Cancer Research, 15(19):6002–6007.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Idh1 mutations as molecular signature and predictive factor of secondary glioblastomas.Clinical Cancer Research, 15(19):6002–6007

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.291031Z

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Observation 15f1c25e-7d3d-42fa-b016-a7ca52f33d71 · outbound

This paper cites Cross- modal alignment and translation for missing modality ac- tion recognition.Computer Vision and Image Understand- ing, 236:103805.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Cross- modal alignment and translation for missing modality ac- tion recognition.Computer Vision and Image Understand- ing, 236:103805

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.246715Z

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Observation 4751828d-ab0a-4f97-984d-15d7463f9195 · outbound

This paper cites Balanced meta-softmax for long- tailed visual recognition.Advances in neural information processing systems, 33:4175–4186.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Balanced meta-softmax for long- tailed visual recognition.Advances in neural information processing systems, 33:4175–4186

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.293673Z

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Observation 22aec4b0-2cc4-4827-8dc5-38410409501a · outbound

This paper cites Cytran: A cycle-consistent transformer with multi-level consistency for non-contrast to contrast ct translation.Neurocomputing, 538:126211.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Cytran: A cycle-consistent transformer with multi-level consistency for non-contrast to contrast ct translation.Neurocomputing, 538:126211

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.297466Z

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Observation 1285e2ba-0667-43a5-bada-5f019e85b719 · outbound

This paper cites Beyond invasive biopsies: us- ing vasari mri features to predict grade and molecular pa- rameters in gliomas.Cancer Imaging, 24(1):3.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Beyond invasive biopsies: us- ing vasari mri features to predict grade and molecular pa- rameters in gliomas.Cancer Imaging, 24(1):3

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.267178Z

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Observation 2a4d1083-87f9-46b7-9be7-d0ce9756b2e8 · outbound

This paper cites Variational mixture-of-experts autoencoders for multi- modal deep generative models.Advances in neural infor- mation processing systems, 32.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Variational mixture-of-experts autoencoders for multi- modal deep generative models.Advances in neural infor- mation processing systems, 32

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.237066Z

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Observation 856f1ca2-3568-4611-9eb5-4bfa65230829 · outbound

This paper cites Passion: Towards effective incomplete multi-modal medical image segmen- tation with imbalanced missing rates.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Passion: Towards effective incomplete multi-modal medical image segmen- tation with imbalanced missing rates

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.235333Z

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Observation da391231-c137-44e8-b42d-dce1c7871835 · outbound

This paper cites Glioma subtype prediction based on ra- diomics of tumor and peritumoral edema under automatic segmentation.Scientific Reports, 14(1):27471.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Glioma subtype prediction based on ra- diomics of tumor and peritumoral edema under automatic segmentation.Scientific Reports, 14(1):27471

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.277198Z

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Observation d30ed9c2-27a4-4fa0-b453-fc59ca87de60 · outbound

This paper cites Self-supervised pre-training of swin transformers for 3d medical image analysis.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Self-supervised pre-training of swin transformers for 3d medical image analysis

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.281074Z

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Observation 9e03f4ae-931f-43bc-89c8-bfc476dbbfc8 · outbound

This paper cites Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning.Neuro-oncology, 25(2):279–289.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning.Neuro-oncology, 25(2):279–289

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.294195Z

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Observation f5c072f1-8fc4-4564-8919-5318dffa8bdd · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Attention is all you need.Advances in neural information processing systems, 30

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.321897Z

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Observation 004b14ae-4081-412d-a6fe-64f9a83571c2 · outbound

This paper cites T5-based model for abstractive summariza- tion: A semi-supervised learning approach with consis- tency loss functions.Applied Sciences, 13(12):7111.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences T5-based model for abstractive summariza- tion: A semi-supervised learning approach with consis- tency loss functions.Applied Sciences, 13(12):7111

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.223651Z

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Observation 10df4a18-7389-4f86-9b0c-33aa0f4d682b · outbound

This paper cites Swin transformer improves the idh mutation status prediction of gliomas free of mri-based tumor segmentation.Journal of Clini- cal Medicine, 11(15):4625.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Swin transformer improves the idh mutation status prediction of gliomas free of mri-based tumor segmentation.Journal of Clini- cal Medicine, 11(15):4625

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.218782Z

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Observation cce45853-84d9-4d18-bca4-27eed88b243a · outbound

This paper cites Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma pa- tients.NPJ Precision Oncology, 8(1):181.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma pa- tients.NPJ Precision Oncology, 8(1):181

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.227311Z

Source-reported events for the cited work

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Observation 1e38cd6b-8d9a-4f9c-96ba-14d2bdc323d7 · outbound

This paper cites Rethinking masked image modelling for medical image representation.Medi- cal Image Analysis, 98:103304.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Rethinking masked image modelling for medical image representation.Medi- cal Image Analysis, 98:103304

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.205723Z

Source-reported events for the cited work

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Observation 59345623-915f-41f2-9d88-87fb5d491986 · outbound

This paper cites Leveraging knowledge of modality experts for in- complete multimodal learning.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Leveraging knowledge of modality experts for in- complete multimodal learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.208064Z

Source-reported events for the cited work

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Observation 5437aff4-f5f1-43ff-9ab9-5fd7149411b4 · outbound

This paper cites Mcmoe: Complet- ing missing modalities with mixture of experts for incom- plete multimodal action quality assessment.arXiv preprint arXiv:2511.17397.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Mcmoe: Complet- ing missing modalities with mixture of experts for incom- plete multimodal action quality assessment.arXiv preprint arXiv:2511.17397

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-25T03:26:35.624604Z

Source-reported events for the cited work

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Observation 12af5f97-8ffc-4a82-ad80-07b119b06abf · outbound

This paper cites Predicting the molecular subtypes of 2021 who grade 4 glioma by a mul- tiparametric mri-based machine learning model.BMC cancer, 25(1):1171.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Predicting the molecular subtypes of 2021 who grade 4 glioma by a mul- tiparametric mri-based machine learning model.BMC cancer, 25(1):1171

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.240175Z

Source-reported events for the cited work

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Observation 3fa57bb1-907f-4c4f-9330-5c58cdea92b2 · outbound

This paper cites Gain: Missing data imputation using gen- erative adversarial nets.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Gain: Missing data imputation using gen- erative adversarial nets

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.287285Z

Source-reported events for the cited work

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Observation 3180a4fc-c121-4701-8fbb-81092a451f93 · outbound

This paper cites GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-05-25T03:26:35.633233Z

Source-reported events for the cited work

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Observation d40f1dd8-0aec-408a-9b67-b4777dea6743 · outbound

This paper cites Deep long-tailed learn- ing: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(9):10795–10816.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Deep long-tailed learn- ing: A survey.IEEE transactions on pattern analysis and machine intelligence, 45(9):10795–10816

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.214909Z

Source-reported events for the cited work

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Observation 709e2f9e-2fdc-4bf2-943f-1bb1f3472444 · outbound

This paper cites Deep learning-based reconstruction on intensity-inhomogeneous diffusion magnetic resonance imaging.Iradiology, 2(6):571–583.

GMENet: Generative Mixture of Experts Network for Multi-Center Glioma Diagnosis with Incomplete Imaging Sequences Deep learning-based reconstruction on intensity-inhomogeneous diffusion magnetic resonance imaging.Iradiology, 2(6):571–583

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T03:26:36.211722Z

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