Missing MRI Pulse Sequence Synthesis Using Multi-Modal Generative Adversarial Network

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Perceptual Losses for Real-Time Style Transfer and Super-Resolution

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Diffusion models in medical imaging: A comprehensive survey

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ResViT: Residual Vision Transformers for Multimodal Medical Image Synthesis

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Unsupervised Medical Image Translation With Adversarial Diffusion Models

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Data augmentation for medical imaging: A systematic literature review

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Deep learning based synthesis of MRI, CT and PET: Review and analysis

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Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review

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mustGAN: multi-stream Generative Adversarial Networks for MR Image Synthesis

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Prior-Guided Image Reconstruction for Accelerated Multi-Contrast MRI via Generative Adversarial Networks

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Generative-AI-Driven Human Digital Twin in IoT Healthcare: A Comprehensive Survey

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Generative Adversarial Networks to Synthesize Missing T1 and FLAIR MRI Sequences for Use in a Multisequence Brain Tumor Segmentation Model

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One Model to Synthesize Them All: Multi-Contrast Multi-Scale Transformer for Missing Data Imputation

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Generative AI for brain image computing and brain network computing: a review

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Learning With Privileged Multimodal Knowledge for Unimodal Segmentation

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CoLa-Diff: Conditional Latent Diffusion Model for Multi-modal MRI Synthesis

Jiang, L., Mao, Y., Wang, X., Chen, X., & Li, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43999-5_38 · Cited by 108

Autoencoder based self-supervised test-time adaptation for medical image analysis

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ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities

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Multi-Domain Image Completion for Random Missing Input Data

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The role of generative adversarial networks in brain MRI: a scoping review

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Multimodal MRI synthesis using unified generative adversarial networks

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DiamondGAN: Unified Multi-modal Generative Adversarial Networks for MRI Sequences Synthesis

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Multi-Modal Modality-Masked Diffusion Network for Brain MRI Synthesis With Random Modality Missing

Meng, X., Sun, K., Xu, J., He, X., & Shen, D. G. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3368664 · Cited by 94

Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation

Reza, M. K., Prater-Bennette, A., & Asif, M. S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3476487 · Cited by 78

Brain stroke lesion segmentation using consistent perception generative adversarial network

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Similarity and quality metrics for MR image-to-image translation

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Unified Multi-Modal Image Synthesis for Missing Modality Imputation

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Self-consistent recursive diffusion bridge for medical image translation

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FedMed-GAN: Federated domain translation on unsupervised cross-modality brain image synthesis

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A literature survey of MR-based brain tumor segmentation with missing modalities

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MFTrans: Modality-Masked Fusion Transformer for Incomplete Multi-Modality Brain Tumor Segmentation

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DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency

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Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages

Liu, Y., Yue, L., Xiao, S., Yang, W., Shen, D. G., & Liu, M. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102266 · Cited by 59

Synthesize High-Quality Multi-Contrast Magnetic Resonance Imaging From Multi-Echo Acquisition Using Multi-Task Deep Generative Model

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Cross-Modal Distillation to Improve MRI-Based Brain Tumor Segmentation With Missing MRI Sequences

Rahimpour, M., Bertels, J., Radwan, A., Vandermeulen, H., Sunaert, S. G., Vandermeulen, D., Maes, F., Goffin, K. E., & Koole, M. J. B. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3137561 · Cited by 56

Artificial intelligence in glioma imaging: Challenges and advances

Jin, W., Fatehi, M., Abhishek, K., Mallya, M., Toyota, B., & Hamarneh, G. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab8131 · Cited by 55

One model to unite them all: Personalized federated learning of multi-contrast MRI synthesis

Dalmaz, O., Mirza, M. U., Elmas, G., Özbey, M., Dar, S. U. H., Ceyani, E., Oğuz, K. K., Avestimehr, S., & Çukur, T. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103121 · Cited by 54

Scalable Swin Transformer network for brain tumor segmentation from incomplete MRI modalities

Zhang, D., Wang, C., Chen, T., Chen, W., & Shen, Y. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102788 · Cited by 50

Generative adversarial networks for biomedical time series forecasting and imputation

Festag, S., Denzler, J., & Spreckelsen, C. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104058 · Cited by 46

A transformer-based deep-learning approach for classifying brain metastases into primary organ sites using clinical whole-brain MRI images

Lyu, Q., Namjoshi, S. V., McTyre, E., Topaloĝlu, Ü., Barcus, R. A., Chan, M. D., Cramer, C. K., Debinski, W., Gürcan, M. N., Lesser, G. J., et al. (2022) · Patterns · doi:10.1016/j.patter.2022.100613 · Cited by 45

MOdel-Based SyntheTic Data-Driven Learning (MOST-DL): Application in Single-Shot T 2 Mapping With Severe Head Motion Using Overlapping-Echo Acquisition

Yang, Q., Lin, Y., Wang, J., Bao, J., Wang, X., Ma, L., Zhou, Z., Yang, Q., Cai, S., He, H., et al. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3179981 · Cited by 48

High tissue contrast image synthesis via multistage attention-GAN: Application to segmenting brain MR scans

Hamghalam, M., Wang, T., & Lei, B. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.08.014 · Cited by 43

Deep learning‐based convolutional neural network for intramodality brain MRI synthesis

Osman, A. F. I., & Tamam, N. M. (2022) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.13530 · Cited by 43

Learning Unified Hyper-Network for Multi-Modal MR Image Synthesis and Tumor Segmentation With Missing Modalities

Yang, H., Sun, J. Z., & Xu, Z. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3301934 · Cited by 40

Common feature learning for brain tumor MRI synthesis by context-aware generative adversarial network

Huang, P., Li, D., Jiao, Z., Wei, D., Cao, B., Mo, Z., Wang, Q., Zhang, H., & Shen, D. G. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102472 · Cited by 40

MC 2 ‐Net: motion correction network for multi‐contrast brain MRI

Lee, J., Kim, B., & Park, H. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28719 · Cited by 41

Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation

Maheshwari, H., Liu, Y.-C., & Kira, Z. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv57701.2024.00106 · Cited by 37

Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery

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Semi-Supervised Learning of MRI Synthesis Without Fully-Sampled Ground Truths

Yurt, M., Dalmaz, O., Dar, S. U. H., Özbey, M., Tınaz, B., Oğuz, K. K., & Çukur, T. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3199155 · Cited by 35

Deep learning‐based multi‐modal computing with feature disentanglement for MRI image synthesis

Fei, Y., Zhan, B., Hong, M., Wu, X., Zhou, J., & Wang, Y. (2021) · Medical Physics · doi:10.1002/mp.14929 · Cited by 34

Style transfer in conditional GANs for cross-modality synthesis of brain magnetic resonance images

Qin, Z., Liu, Z., Zhu, P., & Ling, W. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105928 · Cited by 34

Synthesis-based imaging-differentiation representation learning for multi-sequence 3D/4D MRI

Han, L., Tan, T., Zhang, T., Huang, Y., Wang, X., Gao, Y., Teuwen, J., & Mann, R. M. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103044 · Cited by 33

Enhancing Modality-Agnostic Representations via Meta-learning for Brain Tumor Segmentation

Konwer, A., Hu, X., Bae, J., Xu, X., Chen, C., & Prasanna, P. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01958 · Cited by 33

Multi-teacher cross-modal distillation with cooperative deep supervision fusion learning for unimodal segmentation

Ahmad, S., Ullah, Z., & Gwak, J. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111854 · Cited by 33

A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI

Tavse, S., Vijayakumar, V., Bachute, M. R., Gite, S., & Kotecha, K. V. (2022) · Future Internet · doi:10.3390/fi14120351 · Cited by 33

Deep learning for brain disorders: from data processing to disease treatment

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A novel one-to-multiple unsupervised domain adaptation framework for abdominal organ segmentation

Xu, X., Chen, Y., Wu, J., Lu, J., Ye, Y., Huang, Y., Dou, X., Li, K., Wang, G., Zhang, S., et al. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102873 · Cited by 28

Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-modal Glioma Segmentation

Hamghalam, M., Frangi, A. F., Lei, B., & Simpson, A. L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87234-2_42 · Cited by 28

Brain Tumor Segmentation for Multi-Modal MRI with Missing Information

Feng, X., Ghimire, K., Kim, D. D., Chandra, R. S., Zhang, H., Peng, J., Han, B., Huang, G., Chen, Q., Patel, S. H., et al. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00860-7 · Cited by 26

Brain MRI Tumor Segmentation with Adversarial Networks

Giacomello, E., Loiacono, D., & Mainardi, L. T. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207220 · Cited by 35

Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field

Bonada, M., Rossi, L. F., Carone, G., Panico, F., Cofano, F., Fiaschi, P., Garbossa, D., Meco, F. D., & Bianconi, A. (2024) · Biomedicines · doi:10.3390/biomedicines12081878 · Cited by 24

A Unified Multi-Phase CT Synthesis and Classification Framework for Kidney Cancer Diagnosis With Incomplete Data

Uhm, K.-H., Jung, S., Choi, M. H., Hong, S., & Ko, S.-J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3219123 · Cited by 24

MIL normalization —— prerequisites for accurate MRI radiomics analysis

Hu, Z., Zhuang, Q., Xiao, Y., Wu, G., Shi, Z., Chen, L., Wang, Y., & Yu, J. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104403 · Cited by 23

FLAIR MRI sequence synthesis using squeeze attention generative model for reliable brain tumor segmentation

Al-Fakih, A., Shazly, A., Mohammed, A., Elbushnaq, M., Ryu, K., Gu, Y. H., Al‐masni, M. A., & Makary, M. M. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.05.008 · Cited by 22

Fast Multi-Contrast MRI Acquisition by Optimal Sampling of Information Complementary to Pre-Acquired MRI Contrast

Yang, J., Li, X., Liu, F., Nie, D., Lió, P., Qi, H., & Shen, D. G. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3227262 · Cited by 22

Contrast‐enhanced MRI synthesis using dense‐dilated residual convolutions based 3D network toward elimination of gadolinium in neuro‐oncology

Osman, A. F. I., & Tamam, N. M. (2023) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.14120 · Cited by 22

Simulator-generated training datasets as an alternative to using patient data for machine learning: An example in myocardial segmentation with MRI

Xanthis, C. G., Filos, D., Haris, K., & Aletras, A. H. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105817 · Cited by 22

Cross-modality Neuroimage Synthesis: A Survey

Xie, G., Huang, Y., Wang, J., Lyu, J., Zheng, F., Zheng, Y., & Jin, Y. (2023) · ACM Computing Surveys · doi:10.1145/3625227 · Cited by 21

Application of generative adversarial networks in image, face reconstruction and medical imaging: challenges and the current progress

Sabnam, S., & Rajagopal, S. (2024) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2024.2330524 · Cited by 26

PASSION: Towards Effective Incomplete Multi-Modal Medical Image Segmentation with Imbalanced Missing Rates

Shi, J., Shang, C., Sun, Z., Yu, L. C., Yang, X. Y., & Yan, Z. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681543 · Cited by 21

Reconstruction of missing channel in electroencephalogram using spatiotemporal correlation-based averaging

Bahador, N., Jokelainen, J., Mustola, S. T., & Kortelainen, J. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac23e2 · Cited by 20

A Learnable Variational Model for Joint Multimodal MRI Reconstruction and Synthesis

Bian, W., Zhang, Q., Ye, X., & Chen, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16446-0_34 · Cited by 20

Artificial Contrast

Haase, R., Pinetz, T., Kobler, E., Paech, D., Effland, A., Radbruch, A., & Deike‐Hofmann, K. (2023) · Investigative Radiology · doi:10.1097/rli.0000000000000963 · Cited by 21

ARiViT: attention-based residual-integrated vision transformer for noisy brain medical image classification

Hameed, M., Zameer, A., Khan, S. H., & Raja, M. A. Z. (2024) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-024-05220-0 · Cited by 20

Unified Brain MR-Ultrasound Synthesis Using Multi-modal Hierarchical Representations

Dorent, R., Haouchine, N., Kögl, F., Joutard, S., Juvekar, P., Torio, E., Golby, A. J., Sebastien Ourselin, A., Frisken, S. F., Vercauteren, T. K., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43999-5_43 · Cited by 19

Improved generative adversarial imputation networks for missing data

Qin, X., Shi, H., Dong, X., Zhang, S., & Yuan, L. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05814-2 · Cited by 19

High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Segmentation

Hamghalam, M., Lei, B., & Wang, T. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5825 · Cited by 18

An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis

Han, L., Zhang, T., Huang, Y., Dou, H., Wang, X., Gao, Y., Lu, C., Tan, T., & Mann, R. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43999-5_5 · Cited by 17

Using a generative adversarial network to generate synthetic MRI images for multi-class automatic segmentation of brain tumors

Raut, P., Baldini, G., Schöneck, M., & Caldeira, L. (2024) · Frontiers in Radiology · doi:10.3389/fradi.2023.1336902 · Cited by 17

CT synthesis from MR images using frequency attention conditional generative adversarial network

Wei, K., Kong, W., Liu, L., Wang, J., Li, B., Zhao, B., Li, Z., Zhu, J., & Yu, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.107983 · Cited by 17

Bridging MRI Cross-Modality Synthesis and Multi-Contrast Super-Resolution by Fine-Grained Difference Learning

Feng, Y., Deng, S., Lyu, J., Cai, J., Wei, M.-Q., & Qin, J. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3445969 · Cited by 20

Cycle-consistent adversarial networks improves generalizability of radiomics model in grading meningiomas on external validation

Park, Y. W., Shin, S. J., Eom, J., Lee, H., You, S. C., Ahn, S. S., Lim, S. M., Park, R. W., & Lee, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-10956-9 · Cited by 17

Toward general text-guided multimodal brain MRI synthesis for diagnosis and medical image analysis

Wang, Y., Xiong, H., Sun, K., Bai, S., Dai, L., Ding, Z., Liu, J., Wang, Q., Liu, Q., & Shen, D. G. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102182 · Cited by 25

DGSSA: Domain generalization with structural and stylistic augmentation for retinal vessel segmentation

Liu, B., Zhang, Y., Wang, S., Li, S., & Hong, J. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108118 · Cited by 17

Learning a robust RGB-Thermal detector for extreme modality imbalance

Tian, C., Yang, C., Zhu, G., Wang, Q., & He, Z. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.05.005 · Cited by 19

A unified hybrid transformer for joint MRI sequences super-resolution and missing data imputation

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